2021 |
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| 1. | Stepišnik, Tomaž; Škrlj, Blaž; Wicker, Jörg; Kocev, Dragi A comprehensive comparison of molecular feature representations for use in predictive modeling Journal Article In: Computers in Biology and Medicine, 130 , pp. 104197, 2021, ISSN: 0010-4825. Abstract | Links | BibTeX | Altmetric | Tags: biodegradation, cheminformatics, computational sustainability, data mining, enviPath, machine learning, metabolic pathways, molecular feature representation, toxicity @article{stepisnik2021comprehensive, title = {A comprehensive comparison of molecular feature representations for use in predictive modeling}, author = {Toma\v{z} Stepi\v{s}nik and Bla\v{z} \v{S}krlj and J\"{o}rg Wicker and Dragi Kocev}, url = {http://www.sciencedirect.com/science/article/pii/S001048252030528X}, doi = {10.1016/j.compbiomed.2020.104197}, issn = {0010-4825}, year = {2021}, date = {2021-03-01}, journal = {Computers in Biology and Medicine}, volume = {130}, pages = {104197}, abstract = {Machine learning methods are commonly used for predicting molecular properties to accelerate material and drug design. An important part of this process is deciding how to represent the molecules. Typically, machine learning methods expect examples represented by vectors of values, and many methods for calculating molecular feature representations have been proposed. In this paper, we perform a comprehensive comparison of different molecular features, including traditional methods such as fingerprints and molecular descriptors, and recently proposed learnable representations based on neural networks. Feature representations are evaluated on 11 benchmark datasets, used for predicting properties and measures such as mutagenicity, melting points, activity, solubility, and IC50. Our experiments show that several molecular features work similarly well over all benchmark datasets. The ones that stand out most are Spectrophores, which give significantly worse performance than other features on most datasets. Molecular descriptors from the PaDEL library seem very well suited for predicting physical properties of molecules. Despite their simplicity, MACCS fingerprints performed very well overall. The results show that learnable representations achieve competitive performance compared to expert based representations. However, task-specific representations (graph convolutions and Weave methods) rarely offer any benefits, even though they are computationally more demanding. Lastly, combining different molecular feature representations typically does not give a noticeable improvement in performance compared to individual feature representations.}, keywords = {biodegradation, cheminformatics, computational sustainability, data mining, enviPath, machine learning, metabolic pathways, molecular feature representation, toxicity}, pubstate = {published}, tppubtype = {article} } Machine learning methods are commonly used for predicting molecular properties to accelerate material and drug design. An important part of this process is deciding how to represent the molecules. Typically, machine learning methods expect examples represented by vectors of values, and many methods for calculating molecular feature representations have been proposed. In this paper, we perform a comprehensive comparison of different molecular features, including traditional methods such as fingerprints and molecular descriptors, and recently proposed learnable representations based on neural networks. Feature representations are evaluated on 11 benchmark datasets, used for predicting properties and measures such as mutagenicity, melting points, activity, solubility, and IC50. Our experiments show that several molecular features work similarly well over all benchmark datasets. The ones that stand out most are Spectrophores, which give significantly worse performance than other features on most datasets. Molecular descriptors from the PaDEL library seem very well suited for predicting physical properties of molecules. Despite their simplicity, MACCS fingerprints performed very well overall. The results show that learnable representations achieve competitive performance compared to expert based representations. However, task-specific representations (graph convolutions and Weave methods) rarely offer any benefits, even though they are computationally more demanding. Lastly, combining different molecular feature representations typically does not give a noticeable improvement in performance compared to individual feature representations. | ||
2020 |
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| 2. | Chester, Andrew; Koh, Yun Sing; Wicker, Jörg; Sun, Quan; Lee, Junjae Balancing Utility and Fairness against Privacy in Medical Data Inproceedings In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1226-1233, IEEE, 2020. Abstract | Links | BibTeX | Altmetric | Tags: accuracy, computational sustainability, data mining, fairness, imbalance, machine learning, medicine, privacy @inproceedings{chester2020balancing, title = {Balancing Utility and Fairness against Privacy in Medical Data}, author = {Andrew Chester and Yun Sing Koh and J\"{o}rg Wicker and Quan Sun and Junjae Lee}, url = {https://ieeexplore.ieee.org/abstract/document/9308226}, doi = {10.1109/SSCI47803.2020.9308226}, year = {2020}, date = {2020-12-01}, booktitle = {IEEE Symposium Series on Computational Intelligence (SSCI)}, pages = {1226-1233}, publisher = {IEEE}, abstract = {There are numerous challenges when designing algorithms that interact with sensitive data, such as, medical or financial records. One of these challenges is privacy. However, there is a tension between privacy, utility (model accuracy), and fairness. While de-identification techniques, such as generalisation and suppression, have been proposed to enable privacy protection, it comes with a cost, specifically to fairness and utility. Recent work on fairness in algorithm design defines fairness as a guarantee of similar outputs for "similar" input data. This notion is discussed in connection to de-identification. This research investigates the trade-off between privacy, fairness, and utility. In contrast, other work investigates the trade-off between privacy and utility of the data or accuracy of the model overall. In this research, we investigate the effects of two standard de-identification techniques, k-anonymity and differential privacy, on both utility and fairness. We propose two measures to calculate the trade-off between privacy-utility and privacy-fairness. Although other research has provided guarantees for privacy regarding utility, this research focuses on the trade-offs given set de-identification levels and relies on guarantees provided by the privacy preservation methods. We discuss the effects of de-identification on data of different characteristics, class imbalance and outcome imbalance. We evaluated this is on synthetic datasets and standard real-world datasets. As a case study, we analysed the Medical Expenditure Panel Survey dataset. }, keywords = {accuracy, computational sustainability, data mining, fairness, imbalance, machine learning, medicine, privacy}, pubstate = {published}, tppubtype = {inproceedings} } There are numerous challenges when designing algorithms that interact with sensitive data, such as, medical or financial records. One of these challenges is privacy. However, there is a tension between privacy, utility (model accuracy), and fairness. While de-identification techniques, such as generalisation and suppression, have been proposed to enable privacy protection, it comes with a cost, specifically to fairness and utility. Recent work on fairness in algorithm design defines fairness as a guarantee of similar outputs for "similar" input data. This notion is discussed in connection to de-identification. This research investigates the trade-off between privacy, fairness, and utility. In contrast, other work investigates the trade-off between privacy and utility of the data or accuracy of the model overall. In this research, we investigate the effects of two standard de-identification techniques, k-anonymity and differential privacy, on both utility and fairness. We propose two measures to calculate the trade-off between privacy-utility and privacy-fairness. Although other research has provided guarantees for privacy regarding utility, this research focuses on the trade-offs given set de-identification levels and relies on guarantees provided by the privacy preservation methods. We discuss the effects of de-identification on data of different characteristics, class imbalance and outcome imbalance. We evaluated this is on synthetic datasets and standard real-world datasets. As a case study, we analysed the Medical Expenditure Panel Survey dataset. | ||
| 3. | Dost, Katharina; Taskova, Katerina; Riddle, Pat; Wicker, Jörg Your Best Guess When You Know Nothing: Identification and Mitigation of Selection Bias Inproceedings Forthcoming In: 2020 IEEE International Conference on Data Mining (ICDM), IEEE, Forthcoming. Abstract | BibTeX | Tags: bias, data mining, fairness, machine learning @inproceedings{dost2020your, title = {Your Best Guess When You Know Nothing: Identification and Mitigation of Selection Bias}, author = {Katharina Dost and Katerina Taskova and Pat Riddle and J\"{o}rg Wicker}, year = {2020}, date = {2020-11-17}, booktitle = {2020 IEEE International Conference on Data Mining (ICDM)}, publisher = {IEEE}, abstract = {Machine Learning typically assumes that training and test set are independently drawn from the same distribution, but this assumption is often violated in practice which creates a bias. Many attempts to identify and mitigate this bias have been proposed, but they usually rely on ground-truth information. But what if the researcher is not even aware of the bias? In contrast to prior work, this paper introduces a new method, Imitate, to identify and mitigate Selection Bias in the case that we may not know if (and where) a bias is present, and hence no ground-truth information is available. Imitate investigates the dataset's probability density, then adds generated points in order to smooth out the density and have it resemble a Gaussian, the most common density occurring in real-world applications. If the artificial points focus on certain areas and are not widespread, this could indicate a Selection Bias where these areas are underrepresented in the sample. We demonstrate the effectiveness of the proposed method in both, synthetic and real-world datasets. We also point out limitations and future research directions.}, keywords = {bias, data mining, fairness, machine learning}, pubstate = {forthcoming}, tppubtype = {inproceedings} } Machine Learning typically assumes that training and test set are independently drawn from the same distribution, but this assumption is often violated in practice which creates a bias. Many attempts to identify and mitigate this bias have been proposed, but they usually rely on ground-truth information. But what if the researcher is not even aware of the bias? In contrast to prior work, this paper introduces a new method, Imitate, to identify and mitigate Selection Bias in the case that we may not know if (and where) a bias is present, and hence no ground-truth information is available. Imitate investigates the dataset's probability density, then adds generated points in order to smooth out the density and have it resemble a Gaussian, the most common density occurring in real-world applications. If the artificial points focus on certain areas and are not widespread, this could indicate a Selection Bias where these areas are underrepresented in the sample. We demonstrate the effectiveness of the proposed method in both, synthetic and real-world datasets. We also point out limitations and future research directions. | ||
| 4. | Roeslin, Samuel; Ma, Quincy; and Chigullapally, Pavan; Wicker, Jörg; Wotherspoon, Liam Feature Engineering for a Seismic Loss Prediction Model using Machine Learning, Christchurch Experience Inproceedings Forthcoming In: 17th World Conference on Earthquake Engineering, Forthcoming. Abstract | BibTeX | Tags: computational sustainability, data mining, earthquakes, machine learning @inproceedings{roeslin2020feature, title = { Feature Engineering for a Seismic Loss Prediction Model using Machine Learning, Christchurch Experience}, author = {Samuel Roeslin and Quincy Ma and and Pavan Chigullapally and J\"{o}rg Wicker and Liam Wotherspoon}, year = {2020}, date = {2020-09-17}, booktitle = {17th World Conference on Earthquake Engineering}, abstract = {The city of Christchurch, New Zealand experienced four major earthquakes (MW > 5.9) and multiple aftershocks between 4 September 2010 and 23 December 2011. This series of earthquakes, commonly known as the Canterbury Earthquake Sequence (CES), induced over NZ$40 billion in total economic losses. Liquefaction alone led to building damage in 51,000 of the 140,000 residential buildings, with around 15,000 houses left unpractical to repair. Widespread damage to residential buildings highlighted the need for improved seismic prediction tools and to better understand factors influencing damage. Fortunately, due to New Zealand unique insurance setting, up to 80% of the losses were insured. Over the entire CES, insurers received more than 650,000 claims. This research project employs multi-disciplinary empirical data gathered during and prior to the CES to develop a seismic loss prediction model for residential buildings in Christchurch using machine learning. The intent is to develop a procedure for developing insights from post-earthquake data that is subjected to continuous updating, to enable identification of critical parameters affecting losses, and to apply such a model to establish priority building stock for risk mitigation measures. The following paper describes the complex data preparation process required for the application of machine learning techniques. The paper covers the production of a merged dataset with information from the Earthquake Commission (EQC) claim database, building characteristics from RiskScape, seismic demand interpolated from GeoNet strong motion records, liquefaction occurrence from the New Zealand Geotechnical Database (NZGD) and soil conditions from Land Resource Information Systems (LRIS).}, keywords = {computational sustainability, data mining, earthquakes, machine learning}, pubstate = {forthcoming}, tppubtype = {inproceedings} } The city of Christchurch, New Zealand experienced four major earthquakes (MW > 5.9) and multiple aftershocks between 4 September 2010 and 23 December 2011. This series of earthquakes, commonly known as the Canterbury Earthquake Sequence (CES), induced over NZ$40 billion in total economic losses. Liquefaction alone led to building damage in 51,000 of the 140,000 residential buildings, with around 15,000 houses left unpractical to repair. Widespread damage to residential buildings highlighted the need for improved seismic prediction tools and to better understand factors influencing damage. Fortunately, due to New Zealand unique insurance setting, up to 80% of the losses were insured. Over the entire CES, insurers received more than 650,000 claims. This research project employs multi-disciplinary empirical data gathered during and prior to the CES to develop a seismic loss prediction model for residential buildings in Christchurch using machine learning. The intent is to develop a procedure for developing insights from post-earthquake data that is subjected to continuous updating, to enable identification of critical parameters affecting losses, and to apply such a model to establish priority building stock for risk mitigation measures. The following paper describes the complex data preparation process required for the application of machine learning techniques. The paper covers the production of a merged dataset with information from the Earthquake Commission (EQC) claim database, building characteristics from RiskScape, seismic demand interpolated from GeoNet strong motion records, liquefaction occurrence from the New Zealand Geotechnical Database (NZGD) and soil conditions from Land Resource Information Systems (LRIS). | ||
| 5. | Roeslin, Samuel; Ma, Quincy; Juárez-Garcia, Hugon; Gómez-Bernal, Alonso; Wicker, Jörg; Wotherspoon, Liam A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake Journal Article In: Earthquake Spectra, 36 (2), pp. 314-339, 2020. Abstract | Links | BibTeX | Altmetric | Tags: computational sustainability, data mining, earthquakes, machine learning @article{roeslin2020machine, title = {A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake}, author = {Samuel Roeslin and Quincy Ma and Hugon Ju\'{a}rez-Garcia and Alonso G\'{o}mez-Bernal and J\"{o}rg Wicker and Liam Wotherspoon}, doi = {https://doi.org/10.1177/8755293020936714}, year = {2020}, date = {2020-07-30}, journal = {Earthquake Spectra}, volume = {36}, number = {2}, pages = {314-339}, abstract = {The 2017 Puebla, Mexico, earthquake event led to significant damage in many buildings in Mexico City. In the months following the earthquake, civil engineering students conducted detailed building assessments throughout the city. They collected building damage information and structural characteristics for 340 buildings in the Mexico City urban area, with an emphasis on the Roma and Condesa neighborhoods where they assessed 237 buildings. These neighborhoods are of particular interest due to the availability of seismic records captured by nearby recording stations, and preexisting information from when the neighborhoods were affected by the 1985 Michoac\'{a}n earthquake. This article presents a case study on developing a damage prediction model using machine learning. It details a framework suitable for working with future post-earthquake observation data. Four algorithms able to perform classification tasks were trialed. Random forest, the best performing algorithm, achieves more than 65% prediction accuracy. The study of the feature importance for the random forest shows that the building location, seismic demand, and building height are the parameters that influence the model output the most.}, keywords = {computational sustainability, data mining, earthquakes, machine learning}, pubstate = {published}, tppubtype = {article} } The 2017 Puebla, Mexico, earthquake event led to significant damage in many buildings in Mexico City. In the months following the earthquake, civil engineering students conducted detailed building assessments throughout the city. They collected building damage information and structural characteristics for 340 buildings in the Mexico City urban area, with an emphasis on the Roma and Condesa neighborhoods where they assessed 237 buildings. These neighborhoods are of particular interest due to the availability of seismic records captured by nearby recording stations, and preexisting information from when the neighborhoods were affected by the 1985 Michoacán earthquake. This article presents a case study on developing a damage prediction model using machine learning. It details a framework suitable for working with future post-earthquake observation data. Four algorithms able to perform classification tasks were trialed. Random forest, the best performing algorithm, achieves more than 65% prediction accuracy. The study of the feature importance for the random forest shows that the building location, seismic demand, and building height are the parameters that influence the model output the most. | ||
2019 |
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| 6. | Wicker, Jörg; Hua, Yan Cathy; Rebello, Rayner; Pfahringer, Bernhard XOR-based Boolean Matrix Decomposition Inproceedings In: Wang, Jianyong; Shim, Kyuseok; Wu, Xindong (Ed.): 2019 IEEE International Conference on Data Mining (ICDM), pp. 638-647, IEEE, 2019, ISBN: 978-1-7281-4604-1. Abstract | Links | BibTeX | Altmetric | Tags: Boolean matrix decomposition, data mining @inproceedings{wicker2019xor, title = {XOR-based Boolean Matrix Decomposition}, author = {J\"{o}rg Wicker and Yan Cathy Hua and Rayner Rebello and Bernhard Pfahringer}, editor = {Jianyong Wang and Kyuseok Shim and Xindong Wu}, url = {https://ieeexplore.ieee.org/document/8970951}, doi = {10.1109/ICDM.2019.00074}, isbn = {978-1-7281-4604-1}, year = {2019}, date = {2019-11-08}, booktitle = {2019 IEEE International Conference on Data Mining (ICDM)}, pages = {638-647}, publisher = {IEEE}, abstract = {Boolean matrix factorization (BMF) is a data summarizing and dimension-reduction technique. Existing BMF methods build on matrix properties defined by Boolean algebra, where the addition operator is the logical inclusive OR and the multiplication operator the logical AND. As a consequence, this leads to the lack of an additive inverse in all Boolean matrix operations, which produces an indelible type of approximation error. Previous research adopted various methods to address such an issue and produced reasonably accurate approximation. However, an exact factorization is rarely found in the literature. In this paper, we introduce a new algorithm named XBMaD (Xor-based Boolean Matrix Decomposition) where the addition operator is defined as the exclusive OR (XOR). This change completely removes the error-mitigation issue of OR-based BMF methods, and allows for an exact error-free factorization. An evaluation comparing XBMaD and classic OR-based methods suggested that XBMAD performed equal or in most cases more accurately and faster. }, keywords = {Boolean matrix decomposition, data mining}, pubstate = {published}, tppubtype = {inproceedings} } Boolean matrix factorization (BMF) is a data summarizing and dimension-reduction technique. Existing BMF methods build on matrix properties defined by Boolean algebra, where the addition operator is the logical inclusive OR and the multiplication operator the logical AND. As a consequence, this leads to the lack of an additive inverse in all Boolean matrix operations, which produces an indelible type of approximation error. Previous research adopted various methods to address such an issue and produced reasonably accurate approximation. However, an exact factorization is rarely found in the literature. In this paper, we introduce a new algorithm named XBMaD (Xor-based Boolean Matrix Decomposition) where the addition operator is defined as the exclusive OR (XOR). This change completely removes the error-mitigation issue of OR-based BMF methods, and allows for an exact error-free factorization. An evaluation comparing XBMaD and classic OR-based methods suggested that XBMAD performed equal or in most cases more accurately and faster. | ||
| 7. | Jonauskaite, Domicele; Wicker, Jörg; Mohr, Chrisine; Dael, Nele; Havelka, Jelena; Papadatou-Pastou, Marietta; Zhang, Meng; Oberfeld, Daniel A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences Journal Article In: Royal Society Open Science, 6 (9), pp. 190741, 2019. Abstract | Links | BibTeX | Altmetric | Tags: emotion, machine learning, psychology @article{Jonauskaite2019, title = {A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences}, author = {Domicele Jonauskaite and J\"{o}rg Wicker and Chrisine Mohr and Nele Dael and Jelena Havelka and Marietta Papadatou-Pastou and Meng Zhang and Daniel Oberfeld }, editor = {Andrew Dunn}, url = {https://royalsocietypublishing.org/doi/10.1098/rsos.190741 https://doi.org/10.1098/rsos.190741}, doi = {10.1098/rsos.190741}, year = {2019}, date = {2019-09-25}, journal = {Royal Society Open Science}, volume = {6}, number = {9}, pages = {190741}, abstract = {The link between colour and emotion and its possible similarity across cultures are questions that have not been fully resolved. Online, 711 participants from China, Germany, Greece and the UK associated 12 colour terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of colour-emotion associations and (b) the degree to which they are country-specific, on the basis of the accuracy of a statistical classifier in (a) decoding the colour term evaluated on a given trial from the 20 ratings of colour-emotion associations and (b) predicting the country of origin from the 240 individual colour-emotion associations, respectively. The classifier accuracies were significantly above chance level, demonstrating that emotion associations are to some extent colour-specific and that colour-emotion associations are to some extent country-specific. A second measure of country-specificity, the in-group advantage of the colour-decoding accuracy, was detectable but relatively small (6.1%), indicating that colour-emotion associations are both universal and culture-specific. Our results show that machine learning is a promising tool when analysing complex datasets from emotion research.}, keywords = {emotion, machine learning, psychology}, pubstate = {published}, tppubtype = {article} } The link between colour and emotion and its possible similarity across cultures are questions that have not been fully resolved. Online, 711 participants from China, Germany, Greece and the UK associated 12 colour terms with 20 discrete emotion terms in their native languages. We propose a machine learning approach to quantify (a) the consistency and specificity of colour-emotion associations and (b) the degree to which they are country-specific, on the basis of the accuracy of a statistical classifier in (a) decoding the colour term evaluated on a given trial from the 20 ratings of colour-emotion associations and (b) predicting the country of origin from the 240 individual colour-emotion associations, respectively. The classifier accuracies were significantly above chance level, demonstrating that emotion associations are to some extent colour-specific and that colour-emotion associations are to some extent country-specific. A second measure of country-specificity, the in-group advantage of the colour-decoding accuracy, was detectable but relatively small (6.1%), indicating that colour-emotion associations are both universal and culture-specific. Our results show that machine learning is a promising tool when analysing complex datasets from emotion research. | ||
| 8. | Roeslin, Samuel; Ma, Quincy; Wicker, Jörg; Wotherspoon, Liam Data integration for the development of a seismic loss prediction model for residential buildings in New Zealand Inproceedings In: Cellier, Peggy; Driessens, Kurt (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 88-100, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-43887-6. Abstract | Links | BibTeX | Altmetric | Tags: computational sustainability, earthquakes @inproceedings{roeslin2019data, title = {Data integration for the development of a seismic loss prediction model for residential buildings in New Zealand}, author = {Samuel Roeslin and Quincy Ma and J\"{o}rg Wicker and Liam Wotherspoon}, editor = {Peggy Cellier and Kurt Driessens}, url = {https://link.springer.com/chapter/10.1007/978-3-030-43887-6_8}, doi = {10.1007/978-3-030-43887-6_8}, isbn = {978-3-030-43887-6}, year = {2019}, date = {2019-09-19}, booktitle = {Machine Learning and Knowledge Discovery in Databases}, pages = {88-100}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {In 2010--2011, New Zealand experienced the most damaging earthquakes in its history. It led to extensive damage to Christchurch buildings, infrastructure and its surroundings; affecting commercial and residential buildings. The direct economic losses represented 20{%} of New Zealand's GDP in 2011. Owing to New Zealand's particular insurance structure, the insurance sector contributed to over 80{%} of losses for a total of more than NZ{$}31 billion. Amongst this, over NZ{$}11 billion of the losses arose from residential building claims and were covered either partially or entirely from the NZ government backed Earthquake Commission (EQC) cover insurance scheme. In the process of resolving the claims, EQC collected detailed financial loss data, post-event observations and building characteristics for each of the approximately 434,000 claims lodged following the Canterbury Earthquake sequence (CES). Added to this, the active NZ earthquake engineering community treated the event as a large scale outdoor experiment and collected extensive data on the ground shaking levels, soil conditions, and liquefaction occurrence throughout wider Christchurch. This paper discusses the necessary data preparation process preceding the development of a machine learning seismic loss model. The process draws heavily upon using Geographic Information System (GIS) techniques to aggregate relevant information from multiple databases interpolating data between categories and converting data between continuous and categorical forms. Subsequently, the database is processed, and a residential seismic loss prediction model is developed using machine learning. The aim is to develop a `grey-box' model enabling human interpretability of the decision steps.}, keywords = {computational sustainability, earthquakes}, pubstate = {published}, tppubtype = {inproceedings} } In 2010--2011, New Zealand experienced the most damaging earthquakes in its history. It led to extensive damage to Christchurch buildings, infrastructure and its surroundings; affecting commercial and residential buildings. The direct economic losses represented 20{%} of New Zealand's GDP in 2011. Owing to New Zealand's particular insurance structure, the insurance sector contributed to over 80{%} of losses for a total of more than NZ{$}31 billion. Amongst this, over NZ{$}11 billion of the losses arose from residential building claims and were covered either partially or entirely from the NZ government backed Earthquake Commission (EQC) cover insurance scheme. In the process of resolving the claims, EQC collected detailed financial loss data, post-event observations and building characteristics for each of the approximately 434,000 claims lodged following the Canterbury Earthquake sequence (CES). Added to this, the active NZ earthquake engineering community treated the event as a large scale outdoor experiment and collected extensive data on the ground shaking levels, soil conditions, and liquefaction occurrence throughout wider Christchurch. This paper discusses the necessary data preparation process preceding the development of a machine learning seismic loss model. The process draws heavily upon using Geographic Information System (GIS) techniques to aggregate relevant information from multiple databases interpolating data between categories and converting data between continuous and categorical forms. Subsequently, the database is processed, and a residential seismic loss prediction model is developed using machine learning. The aim is to develop a `grey-box' model enabling human interpretability of the decision steps. | ||
| 9. | Williams, Jonathan; Stönner, Christof; Edtbauer, Achim; Derstorff, Bettina; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Krauter, Nicolas; Wicker, Jörg; Kramer, Stefan What can we learn from the air chemistry of crowds? Inproceedings In: Hansel, Armin; Dunkl, Jürgen (Ed.): 8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications, pp. 121-123, Innsbruck University Press, Innsbruck, 2019. Abstract | Links | BibTeX | Tags: atmospheric chemistry, breath analysis, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series @inproceedings{williams2019what, title = {What can we learn from the air chemistry of crowds?}, author = {Jonathan Williams and Christof St\"{o}nner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and Nicolas Krauter and J\"{o}rg Wicker and Stefan Kramer}, editor = {Armin Hansel and J\"{u}rgen Dunkl}, url = {https://www.ionicon.com/sites/default/files/uploads/doc/Contributions_8th-PTR-MS-Conference-2019_web.pdf#page=122}, year = {2019}, date = {2019-05-10}, booktitle = {8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications}, pages = {121-123}, publisher = {Innsbruck University Press}, address = {Innsbruck}, abstract = {Current PTR-MS technology allows hundreds of volatile trace gases in air to be measured every second at extremely low levels (parts per trillion). These instruments are often used in atmospheric research on planes and ships and even in the Amazon rainforest. Recently, we have used this technology to examine air composition changes caused by large groups of people (10,000-30,000) under real world conditions at a football match and in a movie theater. In both cases the trace gas signatures measured in ambient air are shown to reflect crowd behavior. By applying advanced data mining techniques we have shown that groups of people reproducibly respond to certain emotional stimuli (e.g. suspense and comedy) by exhaling specific trace gases. Furthermore, we explore whether this information can be used to determine the age classification of films.}, keywords = {atmospheric chemistry, breath analysis, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series}, pubstate = {published}, tppubtype = {inproceedings} } Current PTR-MS technology allows hundreds of volatile trace gases in air to be measured every second at extremely low levels (parts per trillion). These instruments are often used in atmospheric research on planes and ships and even in the Amazon rainforest. Recently, we have used this technology to examine air composition changes caused by large groups of people (10,000-30,000) under real world conditions at a football match and in a movie theater. In both cases the trace gas signatures measured in ambient air are shown to reflect crowd behavior. By applying advanced data mining techniques we have shown that groups of people reproducibly respond to certain emotional stimuli (e.g. suspense and comedy) by exhaling specific trace gases. Furthermore, we explore whether this information can be used to determine the age classification of films. | ||
2018 |
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| 10. | Stönner, Christof; Edtbauer, Achim; Derstorff, Bettina; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Wicker, Jörg; Williams, Jonathan Proof of concept study: Testing human volatile organic compounds as tools for age classification of films Journal Article In: PLOS One, 13 (10), pp. 1-14, 2018. Abstract | Links | BibTeX | Altmetric | Tags: atmospheric chemistry, breath analysis, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series @article{Stonner2018, title = {Proof of concept study: Testing human volatile organic compounds as tools for age classification of films}, author = {Christof St\"{o}nner and Achim Edtbauer and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and J\"{o}rg Wicker and Jonathan Williams}, doi = {10.1371/journal.pone.0203044}, year = {2018}, date = {2018-10-11}, journal = {PLOS One}, volume = {13}, number = {10}, pages = {1-14}, publisher = {Public Library of Science}, abstract = {Humans emit numerous volatile organic compounds (VOCs) through breath and skin. The nature and rate of these emissions are affected by various factors including emotional state. Previous measurements of VOCs and CO2 in a cinema have shown that certain chemicals are reproducibly emitted by audiences reacting to events in a particular film. Using data from films with various age classifications, we have studied the relationship between the emission of multiple VOCs and CO2 and the age classifier (0, 6, 12, and 16) with a view to developing a new chemically based and objective film classification method. We apply a random forest model built with time independent features extracted from the time series of every measured compound, and test predictive capability on subsets of all data. It was found that most compounds were not able to predict all age classifiers reliably, likely reflecting the fact that current classification is based on perceived sensibilities to many factors (e.g. incidences of violence, sex, antisocial behaviour, drug use, and bad language) rather than the visceral biological responses expressed in the data. However, promising results were found for isoprene which reliably predicted 0, 6 and 12 age classifiers for a variety of film genres and audience age groups. Therefore, isoprene emission per person might in future be a valuable aid to national classification boards, or even offer an alternative, objective, metric for rating films based on the reactions of large groups of people.}, keywords = {atmospheric chemistry, breath analysis, cinema data mining, data mining, emotional response analysis, machine learning, movie analysis, smell of fear, sof, time series}, pubstate = {published}, tppubtype = {article} } Humans emit numerous volatile organic compounds (VOCs) through breath and skin. The nature and rate of these emissions are affected by various factors including emotional state. Previous measurements of VOCs and CO2 in a cinema have shown that certain chemicals are reproducibly emitted by audiences reacting to events in a particular film. Using data from films with various age classifications, we have studied the relationship between the emission of multiple VOCs and CO2 and the age classifier (0, 6, 12, and 16) with a view to developing a new chemically based and objective film classification method. We apply a random forest model built with time independent features extracted from the time series of every measured compound, and test predictive capability on subsets of all data. It was found that most compounds were not able to predict all age classifiers reliably, likely reflecting the fact that current classification is based on perceived sensibilities to many factors (e.g. incidences of violence, sex, antisocial behaviour, drug use, and bad language) rather than the visceral biological responses expressed in the data. However, promising results were found for isoprene which reliably predicted 0, 6 and 12 age classifiers for a variety of film genres and audience age groups. Therefore, isoprene emission per person might in future be a valuable aid to national classification boards, or even offer an alternative, objective, metric for rating films based on the reactions of large groups of people. | ||
2017 |
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| 11. | Wicker, Jörg; Kramer, Stefan The Best Privacy Defense is a Good Privacy Offense: Obfuscating a Search Engine User's Profile Journal Article In: Data Mining and Knowledge Discovery, 31 (5), pp. 1419-1443, 2017, ISSN: 1573-756X. Abstract | Links | BibTeX | Altmetric | Tags: adversarial learning, machine learning, personalized ads, privacy, reinforcement learning, search engines @article{wicker2017best, title = {The Best Privacy Defense is a Good Privacy Offense: Obfuscating a Search Engine User's Profile}, author = {J\"{o}rg Wicker and Stefan Kramer}, editor = {Kurt Driessens and Dragi Kocev and Marko Robnik-\v{S}ikonja and Myra Spiliopoulou}, url = {http://rdcu.be/tL0U}, doi = {10.1007/s10618-017-0524-z}, issn = {1573-756X}, year = {2017}, date = {2017-09-01}, journal = {Data Mining and Knowledge Discovery}, volume = {31}, number = {5}, pages = {1419-1443}, abstract = {User privacy on the internet is an important and unsolved problem. So far, no sufficient and comprehensive solution has been proposed that helps a user to protect his or her privacy while using the internet. Data are collected and assembled by numerous service providers. Solutions so far focused on the side of the service providers to store encrypted or transformed data that can be still used for analysis. This has a major flaw, as it relies on the service providers to do this. The user has no chance of actively protecting his or her privacy. In this work, we suggest a new approach, empowering the user to take advantage of the same tool the other side has, namely data mining to produce data which obfuscates the user’s profile. We apply this approach to search engine queries and use feedback of the search engines in terms of personalized advertisements in an algorithm similar to reinforcement learning to generate new queries potentially confusing the search engine. We evaluated the approach using a real-world data set. While evaluation is hard, we achieve results that indicate that it is possible to influence the user’s profile that the search engine generates. This shows that it is feasible to defend a user’s privacy from a new and more practical perspective.}, keywords = {adversarial learning, machine learning, personalized ads, privacy, reinforcement learning, search engines}, pubstate = {published}, tppubtype = {article} } User privacy on the internet is an important and unsolved problem. So far, no sufficient and comprehensive solution has been proposed that helps a user to protect his or her privacy while using the internet. Data are collected and assembled by numerous service providers. Solutions so far focused on the side of the service providers to store encrypted or transformed data that can be still used for analysis. This has a major flaw, as it relies on the service providers to do this. The user has no chance of actively protecting his or her privacy. In this work, we suggest a new approach, empowering the user to take advantage of the same tool the other side has, namely data mining to produce data which obfuscates the user’s profile. We apply this approach to search engine queries and use feedback of the search engines in terms of personalized advertisements in an algorithm similar to reinforcement learning to generate new queries potentially confusing the search engine. We evaluated the approach using a real-world data set. While evaluation is hard, we achieve results that indicate that it is possible to influence the user’s profile that the search engine generates. This shows that it is feasible to defend a user’s privacy from a new and more practical perspective. | ||
| 12. | Latino, Diogo; Wicker, Jörg; Gütlein, Martin; Schmid, Emanuel; Kramer, Stefan; Fenner, Kathrin Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data Journal Article In: Environmental Science: Process & Impact, 2017. Abstract | Links | BibTeX | Altmetric | Tags: biodegradation, cheminformatics, computational sustainability, data mining, enviPath, multi-label classification, REST, web services @article{latino2017eawag, title = {Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data}, author = {Diogo Latino and J\"{o}rg Wicker and Martin G\"{u}tlein and Emanuel Schmid and Stefan Kramer and Kathrin Fenner}, doi = {10.1039/C6EM00697C}, year = {2017}, date = {2017-01-01}, journal = {Environmental Science: Process & Impact}, publisher = {The Royal Society of Chemistry}, abstract = {Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity of f ered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications.}, keywords = {biodegradation, cheminformatics, computational sustainability, data mining, enviPath, multi-label classification, REST, web services}, pubstate = {published}, tppubtype = {article} } Developing models for the prediction of microbial biotransformation pathways and half-lives of trace organic contaminants in different environments requires as training data easily accessible and sufficiently large collections of respective biotransformation data that are annotated with metadata on study conditions. Here, we present the Eawag-Soil package, a public database that has been developed to contain all freely accessible regulatory data on pesticide degradation in laboratory soil simulation studies for pesticides registered in the EU (282 degradation pathways, 1535 reactions, 1619 compounds and 4716 biotransformation half-life values with corresponding metadata on study conditions). We provide a thorough description of this novel data resource, and discuss important features of the pesticide soil degradation data that are relevant for model development. Most notably, the variability of half-life values for individual compounds is large and only about one order of magnitude lower than the entire range of median half-life values spanned by all compounds, demonstrating the need to consider study conditions in the development of more accurate models for biotransformation prediction. We further show how the data can be used to find missing rules relevant for predicting soil biotransformation pathways. From this analysis, eight examples of reaction types were presented that should trigger the formulation of new biotransformation rules, e.g., Ar-OH methylation, or the extension of existing rules e.g., hydroxylation in aliphatic rings. The data were also used to exemplarily explore the dependence of half-lives of different amide pesticides on chemical class and experimental parameters. This analysis highlighted the value of considering initial transformation reactions for the development of meaningful quantitative-structure biotransformation relationships (QSBR), which is a novel opportunity of f ered by the simultaneous encoding of transformation reactions and corresponding half-lives in Eawag-Soil. Overall, Eawag-Soil provides an unprecedentedly rich collection of manually extracted and curated biotransformation data, which should be useful in a great variety of applications. | ||
2016 |
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| 13. | Wicker, Jörg; Fenner, Kathrin; Kramer, Stefan A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction Incollection In: Lässig, Jörg; Kersting, Kristian; Morik, Katharina (Ed.): Computational Sustainability, pp. 75-97, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-31858-5. Abstract | Links | BibTeX | Altmetric | Tags: biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways, multi-label classification @incollection{wicker2016ahybrid, title = {A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction}, author = {J\"{o}rg Wicker and Kathrin Fenner and Stefan Kramer}, editor = {J\"{o}rg L\"{a}ssig and Kristian Kersting and Katharina Morik}, url = {http://dx.doi.org/10.1007/978-3-319-31858-5_5}, doi = {10.1007/978-3-319-31858-5_5}, isbn = {978-3-319-31858-5}, year = {2016}, date = {2016-04-21}, booktitle = {Computational Sustainability}, pages = {75-97}, publisher = {Springer International Publishing}, address = {Cham}, abstract = {One of the main tasks in chemical industry regarding the sustainability of a product is the prediction of its environmental fate, i.e., its degradation products and pathways. Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this chapter, we propose a hybrid knowledge-based and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. Since the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice. Results from leave-one-out cross-validation show that a recall and precision of approximately 0.8 can be achieved for a subset of 13 transformation rules. The set of used rules is further extended using multi-label classification, where dependencies among the transformation rules are exploited to improve the predictions. While the results regarding recall and precision vary, the area under the ROC curve can be improved using multi-label classification. Therefore, it is possible to optimize precision without compromising recall. Recently, we integrated the presented approach into enviPath, a complete redesign and re-implementation of UM-PPS.}, keywords = {biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways, multi-label classification}, pubstate = {published}, tppubtype = {incollection} } One of the main tasks in chemical industry regarding the sustainability of a product is the prediction of its environmental fate, i.e., its degradation products and pathways. Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this chapter, we propose a hybrid knowledge-based and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. Since the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice. Results from leave-one-out cross-validation show that a recall and precision of approximately 0.8 can be achieved for a subset of 13 transformation rules. The set of used rules is further extended using multi-label classification, where dependencies among the transformation rules are exploited to improve the predictions. While the results regarding recall and precision vary, the area under the ROC curve can be improved using multi-label classification. Therefore, it is possible to optimize precision without compromising recall. Recently, we integrated the presented approach into enviPath, a complete redesign and re-implementation of UM-PPS. | ||
| 14. | Wicker, Jörg; Tyukin, Andrey; Kramer, Stefan A Nonlinear Label Compression and Transformation Method for Multi-Label Classification using Autoencoders Inproceedings In: Bailey, James; Khan, Latifur; Washio, Takashi; Dobbie, Gill; Huang, Zhexue Joshua; Wang, Ruili (Ed.): The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 328-340, Springer International Publishing, Switzerland, 2016, ISBN: 978-3-319-31753-3. Abstract | Links | BibTeX | Altmetric | Tags: autoencoders, label compression, machine learning, multi-label classification @inproceedings{wicker2016nonlinear, title = {A Nonlinear Label Compression and Transformation Method for Multi-Label Classification using Autoencoders}, author = {J\"{o}rg Wicker and Andrey Tyukin and Stefan Kramer}, editor = {James Bailey and Latifur Khan and Takashi Washio and Gill Dobbie and Zhexue Joshua Huang and Ruili Wang}, url = {http://dx.doi.org/10.1007/978-3-319-31753-3_27}, doi = {10.1007/978-3-319-31753-3_27}, isbn = {978-3-319-31753-3}, year = {2016}, date = {2016-04-16}, booktitle = {The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD)}, volume = {9651}, pages = {328-340}, publisher = {Springer International Publishing}, address = {Switzerland}, series = {Lecture Notes in Computer Science}, abstract = {Multi-label classification targets the prediction of multiple interdependent and non-exclusive binary target variables. Transformation-based algorithms transform the data set such that regular single-label algorithms can be applied to the problem. A special type of transformation-based classifiers are label compression methods, that compress the labels and then mostly use single label classifiers to predict the compressed labels. So far, there are no compression-based algorithms follow a problem transformation approach and address non-linear dependencies in the labels. In this paper, we propose a new algorithm, called Maniac (Multi-lAbel classificatioN usIng AutoenCoders), which extracts the non-linear dependencies by compressing the labels using autoencoders. We adapt the training process of autoencoders in a way to make them more suitable for a parameter optimization in the context of this algorithm. The method is evaluated on eight standard multi-label data sets. Experiments show that despite not producing a good ranking, Maniac generates a particularly good bipartition of the labels into positives and negatives. This is caused by rather strong predictions with either really high or low probability. Additionally, the algorithm seems to perform better given more labels and a higher label cardinality in the data set.}, keywords = {autoencoders, label compression, machine learning, multi-label classification}, pubstate = {published}, tppubtype = {inproceedings} } Multi-label classification targets the prediction of multiple interdependent and non-exclusive binary target variables. Transformation-based algorithms transform the data set such that regular single-label algorithms can be applied to the problem. A special type of transformation-based classifiers are label compression methods, that compress the labels and then mostly use single label classifiers to predict the compressed labels. So far, there are no compression-based algorithms follow a problem transformation approach and address non-linear dependencies in the labels. In this paper, we propose a new algorithm, called Maniac (Multi-lAbel classificatioN usIng AutoenCoders), which extracts the non-linear dependencies by compressing the labels using autoencoders. We adapt the training process of autoencoders in a way to make them more suitable for a parameter optimization in the context of this algorithm. The method is evaluated on eight standard multi-label data sets. Experiments show that despite not producing a good ranking, Maniac generates a particularly good bipartition of the labels into positives and negatives. This is caused by rather strong predictions with either really high or low probability. Additionally, the algorithm seems to perform better given more labels and a higher label cardinality in the data set. | ||
| 15. | Wicker, Jörg; Lorsbach, Tim; Gütlein, Martin; Schmid, Emanuel; Latino, Diogo; Kramer, Stefan; Fenner, Kathrin enviPath - The Environmental Contaminant Biotransformation Pathway Resource Journal Article In: Nucleic Acid Research, 44 (D1), pp. D502-D508, 2016. Abstract | Links | BibTeX | Altmetric | Tags: biodegradation, cheminformatics, computational sustainability, data mining, enviPath, linked data, machine learning, metabolic pathways, multi-label classification @article{wicker2016envipath, title = {enviPath - The Environmental Contaminant Biotransformation Pathway Resource}, author = {J\"{o}rg Wicker and Tim Lorsbach and Martin G\"{u}tlein and Emanuel Schmid and Diogo Latino and Stefan Kramer and Kathrin Fenner}, editor = {Michael Galperin}, url = {http://nar.oxfordjournals.org/content/44/D1/D502.abstract}, doi = {10.1093/nar/gkv1229}, year = {2016}, date = {2016-01-01}, journal = {Nucleic Acid Research}, volume = {44}, number = {D1}, pages = {D502-D508}, abstract = {The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UM-BBD/PPS. enviPath uses the database from the UM-BBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data.}, keywords = {biodegradation, cheminformatics, computational sustainability, data mining, enviPath, linked data, machine learning, metabolic pathways, multi-label classification}, pubstate = {published}, tppubtype = {article} } The University of Minnesota Biocatalysis/Biodegradation Database and Pathway Prediction System (UM-BBD/PPS) has been a unique resource covering microbial biotransformation pathways of primarily xenobiotic chemicals for over 15 years. This paper introduces the successor system, enviPath (The Environmental Contaminant Biotransformation Pathway Resource), which is a complete redesign and reimplementation of UM-BBD/PPS. enviPath uses the database from the UM-BBD/PPS as a basis, extends the use of this database, and allows users to include their own data to support multiple use cases. Relative reasoning is supported for the refinement of predictions and to allow its extensions in terms of previously published, but not implemented machine learning models. User access is simplified by providing a REST API that simplifies the inclusion of enviPath into existing workflows. An RDF database is used to enable simple integration with other databases. enviPath is publicly available at https://envipath.org with free and open access to its core data. | ||
| 16. | Raza, Atif; Wicker, Jörg; Kramer, Stefan Trading Off Accuracy for Efficiency by Randomized Greedy Warping Inproceedings In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 883-890, ACM, New York, NY, USA, 2016, ISBN: 978-1-4503-3739-7. Abstract | Links | BibTeX | Altmetric | Tags: data mining, dynamic time warping, time series @inproceedings{raza2016trading, title = {Trading Off Accuracy for Efficiency by Randomized Greedy Warping}, author = {Atif Raza and J\"{o}rg Wicker and Stefan Kramer}, url = {https://wicker.nz/nwp-acm/authorize.php?id=N10030 http://doi.acm.org/10.1145/2851613.2851651}, doi = {10.1145/2851613.2851651}, isbn = {978-1-4503-3739-7}, year = {2016}, date = {2016-01-01}, booktitle = {Proceedings of the 31st Annual ACM Symposium on Applied Computing}, pages = {883-890}, publisher = {ACM}, address = {New York, NY, USA}, series = {SAC '16}, abstract = {Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for f i nding similarity between time series instances.We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for very fast classification.}, keywords = {data mining, dynamic time warping, time series}, pubstate = {published}, tppubtype = {inproceedings} } Dynamic Time Warping (DTW) is a widely used distance measure for time series data mining. Its quadratic complexity requires the application of various techniques (e.g. warping constraints, lower-bounds) for deployment in real-time scenarios. In this paper we propose a randomized greedy warping algorithm for f i nding similarity between time series instances.We show that the proposed algorithm outperforms the simple greedy approach and also provides very good time series similarity approximation consistently, as compared to DTW. We show that the Randomized Time Warping (RTW) can be used in place of DTW as a fast similarity approximation technique by trading some classification accuracy for very fast classification. | ||
| 17. | Williams, Jonathan; Stönner, Christof; Wicker, Jörg; Krauter, Nicolas; Derstorff, Bettina; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Kramer, Stefan Cinema audiences reproducibly vary the chemical composition of air during films, by broadcasting scene specific emissions on breath Journal Article In: Scientific Reports, 6 , 2016. Abstract | Links | BibTeX | Altmetric | Tags: atmospheric chemistry, causality, cheminformatics, data mining, emotional response analysis, smell of fear, sof @article{williams2015element, title = {Cinema audiences reproducibly vary the chemical composition of air during films, by broadcasting scene specific emissions on breath}, author = {Jonathan Williams and Christof St\"{o}nner and J\"{o}rg Wicker and Nicolas Krauter and Bettina Derstorff and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and Stefan Kramer}, url = {http://www.nature.com/articles/srep25464}, doi = {10.1038/srep25464}, year = {2016}, date = {2016-01-01}, journal = {Scientific Reports}, volume = {6}, publisher = {Nature Publishing Group}, abstract = {Human beings continuously emit chemicals into the air by breath and through the skin. In order to determine whether these emissions vary predictably in response to audiovisual stimuli, we have continuously monitored carbon dioxide and over one hundred volatile organic compounds in a cinema. It was found that many airborne chemicals in cinema air varied distinctively and reproducibly with time for a particular film, even in different screenings to different audiences. Application of scene labels and advanced data mining methods revealed that specific film events, namely "suspense" or "comedy" caused audiences to change their emission of specific chemicals. These event-type synchronous, broadcasted human chemosignals open the possibility for objective and non-invasive assessment of a human group response to stimuli by continuous measurement of chemicals in air. Such methods can be applied to research fields such as psychology and biology, and be valuable to industries such as film making and advertising.}, keywords = {atmospheric chemistry, causality, cheminformatics, data mining, emotional response analysis, smell of fear, sof}, pubstate = {published}, tppubtype = {article} } Human beings continuously emit chemicals into the air by breath and through the skin. In order to determine whether these emissions vary predictably in response to audiovisual stimuli, we have continuously monitored carbon dioxide and over one hundred volatile organic compounds in a cinema. It was found that many airborne chemicals in cinema air varied distinctively and reproducibly with time for a particular film, even in different screenings to different audiences. Application of scene labels and advanced data mining methods revealed that specific film events, namely "suspense" or "comedy" caused audiences to change their emission of specific chemicals. These event-type synchronous, broadcasted human chemosignals open the possibility for objective and non-invasive assessment of a human group response to stimuli by continuous measurement of chemicals in air. Such methods can be applied to research fields such as psychology and biology, and be valuable to industries such as film making and advertising. | ||
2015 |
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| 18. | Wicker, Jörg; Krauter, Nicolas; Derstorff, Bettina; Stönner, Christof; Bourtsoukidis, Efstratios; Klüpfel, Thomas; Williams, Jonathan; Kramer, Stefan Cinema Data Mining: The Smell of Fear Inproceedings In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235-1304, ACM ACM, New York, NY, USA, 2015, ISBN: 978-1-4503-3664-2. Abstract | Links | BibTeX | Altmetric | Tags: atmospheric chemistry, breath analysis, causality, cinema data mining, data mining, emotional response analysis, movie analysis, smell of fear, sof, time series @inproceedings{wicker2015cinema, title = {Cinema Data Mining: The Smell of Fear}, author = {J\"{o}rg Wicker and Nicolas Krauter and Bettina Derstorff and Christof St\"{o}nner and Efstratios Bourtsoukidis and Thomas Kl\"{u}pfel and Jonathan Williams and Stefan Kramer}, url = {https://wicker.nz/nwp-acm/authorize.php?id=N10031 http://doi.acm.org/10.1145/2783258.2783404}, doi = {10.1145/2783258.2783404}, isbn = {978-1-4503-3664-2}, year = {2015}, date = {2015-01-01}, booktitle = {Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, pages = {1235-1304}, publisher = {ACM}, address = {New York, NY, USA}, organization = {ACM}, series = {KDD '15}, abstract = {While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, ...), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theatre over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs) and for some forms of abductive reasoning and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hints for causality with low p-values can be detected in the data.}, keywords = {atmospheric chemistry, breath analysis, causality, cinema data mining, data mining, emotional response analysis, movie analysis, smell of fear, sof, time series}, pubstate = {published}, tppubtype = {inproceedings} } While the physiological response of humans to emotional events or stimuli is well-investigated for many modalities (like EEG, skin resistance, ...), surprisingly little is known about the exhalation of so-called Volatile Organic Compounds (VOCs) at quite low concentrations in response to such stimuli. VOCs are molecules of relatively small mass that quickly evaporate or sublimate and can be detected in the air that surrounds us. The paper introduces a new field of application for data mining, where trace gas responses of people reacting on-line to films shown in cinemas (or movie theaters) are related to the semantic content of the films themselves. To do so, we measured the VOCs from a movie theatre over a whole month in intervals of thirty seconds, and annotated the screened films by a controlled vocabulary compiled from multiple sources. To gain a better understanding of the data and to reveal unknown relationships, we have built prediction models for so-called forward prediction (the prediction of future VOCs from the past), backward prediction (the prediction of past scene labels from future VOCs) and for some forms of abductive reasoning and Granger causality. Experimental results show that some VOCs and some labels can be predicted with relatively low error, and that hints for causality with low p-values can be detected in the data. | ||
| 19. | Tyukin, Andrey; Kramer, Stefan; Wicker, Jörg Scavenger - A Framework for the Efficient Evaluation of Dynamic and Modular Algorithms Inproceedings In: Bifet, Albert; May, Michael; Zadrozny, Bianca; Gavalda, Ricard; Pedreschi, Dino; Cardoso, Jaime; Spiliopoulou, Myra (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 325-328, Springer International Publishing, 2015, ISBN: 978-3-319-23460-1. Abstract | Links | BibTeX | Altmetric | Tags: autoencoders, distributed processing, framework, large-scale, Scavenger @inproceedings{tyukin2015scavenger, title = {Scavenger - A Framework for the Efficient Evaluation of Dynamic and Modular Algorithms}, author = {Andrey Tyukin and Stefan Kramer and J\"{o}rg Wicker }, editor = {Albert Bifet and Michael May and Bianca Zadrozny and Ricard Gavalda and Dino Pedreschi and Jaime Cardoso and Myra Spiliopoulou}, url = {http://dx.doi.org/10.1007/978-3-319-23461-8_40}, doi = {10.1007/978-3-319-23461-8_40}, isbn = {978-3-319-23460-1}, year = {2015}, date = {2015-01-01}, booktitle = {Machine Learning and Knowledge Discovery in Databases}, volume = {9286}, pages = {325-328}, publisher = {Springer International Publishing}, series = {Lecture Notes in Computer Science}, abstract = {Machine Learning methods and algorithms are often highly modular in the sense that they rely on a large number of subalgorithms that are in principle interchangeable. For example, it is often possible to use various kinds of pre- and post-processing and various base classifiers or regressors as components of the same modular approach. We propose a framework, called Scavenger, that allows evaluating whole families of conceptually similar algorithms efficiently. The algorithms are represented as compositions, couplings and products of atomic subalgorithms. This allows partial results to be cached and shared between different instances of a modular algorithm, so that potentially expensive partial results need not be recomputed multiple times. Furthermore, our framework deals with issues of the parallel execution, load balancing, and with the backup of partial results for the case of implementation or runtime errors. Scavenger is licensed under the GPLv3 and can be downloaded freely at https://github.com/jorro/scavenger.}, keywords = {autoencoders, distributed processing, framework, large-scale, Scavenger}, pubstate = {published}, tppubtype = {inproceedings} } Machine Learning methods and algorithms are often highly modular in the sense that they rely on a large number of subalgorithms that are in principle interchangeable. For example, it is often possible to use various kinds of pre- and post-processing and various base classifiers or regressors as components of the same modular approach. We propose a framework, called Scavenger, that allows evaluating whole families of conceptually similar algorithms efficiently. The algorithms are represented as compositions, couplings and products of atomic subalgorithms. This allows partial results to be cached and shared between different instances of a modular algorithm, so that potentially expensive partial results need not be recomputed multiple times. Furthermore, our framework deals with issues of the parallel execution, load balancing, and with the backup of partial results for the case of implementation or runtime errors. Scavenger is licensed under the GPLv3 and can be downloaded freely at https://github.com/jorro/scavenger. | ||
2014 |
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| 20. | Tyukin, Andrey; Kramer, Stefan; Wicker, Jörg BMaD -- A Boolean Matrix Decomposition Framework Inproceedings In: Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke; Meo, Rosa (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 481-484, Springer Berlin Heidelberg, 2014, ISBN: 978-3-662-44844-1. Abstract | Links | BibTeX | Altmetric | Tags: Boolean matrix decomposition, data mining, framework @inproceedings{tyukin2014bmad, title = {BMaD -- A Boolean Matrix Decomposition Framework}, author = {Andrey Tyukin and Stefan Kramer and J\"{o}rg Wicker}, editor = {Toon Calders and Floriana Esposito and Eyke H\"{u}llermeier and Rosa Meo}, url = {http://dx.doi.org/10.1007/978-3-662-44845-8_40}, doi = {10.1007/978-3-662-44845-8_40}, isbn = {978-3-662-44844-1}, year = {2014}, date = {2014-01-01}, booktitle = {Machine Learning and Knowledge Discovery in Databases}, volume = {8726}, pages = {481-484}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, abstract = {Boolean matrix decomposition is a method to obtain a compressed representation of a matrix with Boolean entries. We present a modular framework that unifies several Boolean matrix decomposition algorithms, and provide methods to evaluate their performance. The main advantages of the framework are its modular approach and hence the flexible combination of the steps of a Boolean matrix decomposition and the capability of handling missing values. The framework is licensed under the GPLv3 and can be downloaded freely at urlhttp://projects.informatik.uni-mainz.de/bmad.}, keywords = {Boolean matrix decomposition, data mining, framework}, pubstate = {published}, tppubtype = {inproceedings} } Boolean matrix decomposition is a method to obtain a compressed representation of a matrix with Boolean entries. We present a modular framework that unifies several Boolean matrix decomposition algorithms, and provide methods to evaluate their performance. The main advantages of the framework are its modular approach and hence the flexible combination of the steps of a Boolean matrix decomposition and the capability of handling missing values. The framework is licensed under the GPLv3 and can be downloaded freely at urlhttp://projects.informatik.uni-mainz.de/bmad. | ||
2013 |
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| 21. | Wicker, Jörg Large Classifier Systems in Bio- and Cheminformatics PhD Thesis Technische Universität München, 2013. Abstract | Links | BibTeX | Tags: biodegradation, bioinformatics, cheminformatics, computational sustainability, data mining, enviPath, machine learning, multi-label classification, multi-relational learning, toxicity @phdthesis{wicker2013large, title = {Large Classifier Systems in Bio- and Cheminformatics}, author = {J\"{o}rg Wicker}, url = {http://mediatum.ub.tum.de/node?id=1165858}, year = {2013}, date = {2013-01-01}, school = {Technische Universit\"{a}t M\"{u}nchen}, abstract = {Large classifier systems are machine learning algorithms that use multiple classifiers to improve the prediction of target values in advanced classification tasks. Although learning problems in bio- and cheminformatics commonly provide data in schemes suitable for large classifier systems, they are rarely used in these domains. This thesis introduces two new classifiers incorporating systems of classifiers using Boolean matrix decomposition to handle data in a schema that often occurs in bio- and cheminformatics. The first approach, called MLC-BMaD (multi-label classification using Boolean matrix decomposition), uses Boolean matrix decomposition to decompose the labels in a multi-label classification task. The decomposed matrices are a compact representation of the information in the labels (first matrix) and the dependencies among the labels (second matrix). The first matrix is used in a further multi-label classification while the second matrix is used to generate the final matrix from the predicted values of the first matrix. MLC-BMaD was evaluated on six standard multi-label data sets, the experiments showed that MLC-BMaD can perform particularly well on data sets with a high number of labels and a small number of instances and can outperform standard multi-label algorithms. Subsequently, MLC-BMaD is extended to a special case of multi-relational learning, by considering the labels not as simple labels, but instances. The algorithm, called ClassFact (Classification factorization), uses both matrices in a multi-label classification. Each label represents a mapping between two instances. Experiments on three data sets from the domain of bioinformatics show that ClassFact can outperform the baseline method, which merges the relations into one, on hard classification tasks. Furthermore, large classifier systems are used on two cheminformatics data sets, the first one is used to predict the environmental fate of chemicals by predicting biodegradation pathways. The second is a data set from the domain of predictive toxicology. In biodegradation pathway prediction, I extend a knowledge-based system and incorporate a machine learning approach to predict a probability for biotransformation products based on the structure- and knowledge-based predictions of products, which are based on transformation rules. The use of multi-label classification improves the performance of the classifiers and extends the number of transformation rules that can be covered. For the prediction of toxic effects of chemicals, I applied large classifier systems to the ToxCasttexttrademark data set, which maps toxic effects to chemicals. As the given toxic effects are not easy to predict due to missing information and a skewed class distribution, I introduce a filtering step in the multi-label classification, which finds labels that are usable in multi-label prediction and does not take the others in the prediction into account. Experiments show that this approach can improve upon the baseline method using binary classification, as well as multi-label approaches using no filtering. The presented results show that large classifier systems can play a role in future research challenges, especially in bio- and cheminformatics, where data sets frequently consist of more complex structures and data can be rather small in terms of the number of instances compared to other domains.}, keywords = {biodegradation, bioinformatics, cheminformatics, computational sustainability, data mining, enviPath, machine learning, multi-label classification, multi-relational learning, toxicity}, pubstate = {published}, tppubtype = {phdthesis} } Large classifier systems are machine learning algorithms that use multiple classifiers to improve the prediction of target values in advanced classification tasks. Although learning problems in bio- and cheminformatics commonly provide data in schemes suitable for large classifier systems, they are rarely used in these domains. This thesis introduces two new classifiers incorporating systems of classifiers using Boolean matrix decomposition to handle data in a schema that often occurs in bio- and cheminformatics. The first approach, called MLC-BMaD (multi-label classification using Boolean matrix decomposition), uses Boolean matrix decomposition to decompose the labels in a multi-label classification task. The decomposed matrices are a compact representation of the information in the labels (first matrix) and the dependencies among the labels (second matrix). The first matrix is used in a further multi-label classification while the second matrix is used to generate the final matrix from the predicted values of the first matrix. MLC-BMaD was evaluated on six standard multi-label data sets, the experiments showed that MLC-BMaD can perform particularly well on data sets with a high number of labels and a small number of instances and can outperform standard multi-label algorithms. Subsequently, MLC-BMaD is extended to a special case of multi-relational learning, by considering the labels not as simple labels, but instances. The algorithm, called ClassFact (Classification factorization), uses both matrices in a multi-label classification. Each label represents a mapping between two instances. Experiments on three data sets from the domain of bioinformatics show that ClassFact can outperform the baseline method, which merges the relations into one, on hard classification tasks. Furthermore, large classifier systems are used on two cheminformatics data sets, the first one is used to predict the environmental fate of chemicals by predicting biodegradation pathways. The second is a data set from the domain of predictive toxicology. In biodegradation pathway prediction, I extend a knowledge-based system and incorporate a machine learning approach to predict a probability for biotransformation products based on the structure- and knowledge-based predictions of products, which are based on transformation rules. The use of multi-label classification improves the performance of the classifiers and extends the number of transformation rules that can be covered. For the prediction of toxic effects of chemicals, I applied large classifier systems to the ToxCasttexttrademark data set, which maps toxic effects to chemicals. As the given toxic effects are not easy to predict due to missing information and a skewed class distribution, I introduce a filtering step in the multi-label classification, which finds labels that are usable in multi-label prediction and does not take the others in the prediction into account. Experiments show that this approach can improve upon the baseline method using binary classification, as well as multi-label approaches using no filtering. The presented results show that large classifier systems can play a role in future research challenges, especially in bio- and cheminformatics, where data sets frequently consist of more complex structures and data can be rather small in terms of the number of instances compared to other domains. | ||
2012 |
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| 22. | Wicker, Jörg; Pfahringer, Bernhard; Kramer, Stefan Multi-label Classification Using Boolean Matrix Decomposition Inproceedings In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 179–186, ACM, 2012, ISBN: 978-1-4503-0857-1. Abstract | Links | BibTeX | Altmetric | Tags: associations, Boolean matrix decomposition, machine learning, multi-label classification @inproceedings{wicker2012multi, title = {Multi-label Classification Using Boolean Matrix Decomposition}, author = {J\"{o}rg Wicker and Bernhard Pfahringer and Stefan Kramer}, url = {https://wicker.nz/nwp-acm/authorize.php?id=N10032 http://doi.acm.org/10.1145/2245276.2245311}, doi = {10.1145/2245276.2245311}, isbn = {978-1-4503-0857-1}, year = {2012}, date = {2012-01-01}, booktitle = {Proceedings of the 27th Annual ACM Symposium on Applied Computing}, pages = {179--186}, publisher = {ACM}, series = {SAC '12}, abstract = {This paper introduces a new multi-label classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them.}, keywords = {associations, Boolean matrix decomposition, machine learning, multi-label classification}, pubstate = {published}, tppubtype = {inproceedings} } This paper introduces a new multi-label classifier based on Boolean matrix decomposition. Boolean matrix decomposition is used to extract, from the full label matrix, latent labels representing useful Boolean combinations of the original labels. Base level models predict latent labels, which are subsequently transformed into the actual labels by Boolean matrix multiplication with the second matrix from the decomposition. The new method is tested on six publicly available datasets with varying numbers of labels. The experimental evaluation shows that the new method works particularly well on datasets with a large number of labels and strong dependencies among them. | ||
2010 |
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| 23. | Hardy, Barry; Douglas, Nicki; Helma, Christoph; Rautenberg, Micha; Jeliazkova, Nina; Jeliazkov, Vedrin; Nikolova, Ivelina; Benigni, Romualdo; Tcheremenskaia, Olga; Kramer, Stefan; Girschick, Tobias; Buchwald, Fabian; Wicker, Jörg; Karwath, Andreas; Gütlein, Martin; Maunz, Andreas; Sarimveis, Haralambos; Melagraki, Georgia; Afantitis, Antreas; Sopasakis, Pantelis; Gallagher, David; Poroikov, Vladimir; Filimonov, Dmitry; Zakharov, Alexey; Lagunin, Alexey; Gloriozova, Tatyana; Novikov, Sergey; Skvortsova, Natalia; Druzhilovsky, Dmitry; Chawla, Sunil; Ghosh, Indira; Ray, Surajit; Patel, Hitesh; Escher, Sylvia Collaborative development of predictive toxicology applications Journal Article In: Journal of Cheminformatics, 2 (1), pp. 7, 2010, ISSN: 1758-2946. Abstract | Links | BibTeX | Altmetric | Tags: cheminformatics, computational sustainability, data mining, machine learning, REST, toxicity @article{hardy2010collaborative, title = {Collaborative development of predictive toxicology applications}, author = {Barry Hardy and Nicki Douglas and Christoph Helma and Micha Rautenberg and Nina Jeliazkova and Vedrin Jeliazkov and Ivelina Nikolova and Romualdo Benigni and Olga Tcheremenskaia and Stefan Kramer and Tobias Girschick and Fabian Buchwald and J\"{o}rg Wicker and Andreas Karwath and Martin G\"{u}tlein and Andreas Maunz and Haralambos Sarimveis and Georgia Melagraki and Antreas Afantitis and Pantelis Sopasakis and David Gallagher and Vladimir Poroikov and Dmitry Filimonov and Alexey Zakharov and Alexey Lagunin and Tatyana Gloriozova and Sergey Novikov and Natalia Skvortsova and Dmitry Druzhilovsky and Sunil Chawla and Indira Ghosh and Surajit Ray and Hitesh Patel and Sylvia Escher}, url = {http://www.jcheminf.com/content/2/1/7}, doi = {10.1186/1758-2946-2-7}, issn = {1758-2946}, year = {2010}, date = {2010-01-01}, journal = {Journal of Cheminformatics}, volume = {2}, number = {1}, pages = {7}, abstract = {OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way.}, keywords = {cheminformatics, computational sustainability, data mining, machine learning, REST, toxicity}, pubstate = {published}, tppubtype = {article} } OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative) Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs) for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals.The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation.Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure-activity relationship modelling of REACH-relevant endpoints: ToxPredict which predicts and reports on toxicities for endpoints for an input chemical structure, and ToxCreate which builds and validates a predictive toxicity model based on an input toxicology dataset. Because of the extensible nature of the standardised Framework design, barriers of interoperability between applications and content are removed, as the user may combine data, models and validation from multiple sources in a dependable and time-effective way. | ||
| 24. | Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach Journal Article In: Bioinformatics, 26 (6), pp. 814-821, 2010. Abstract | Links | BibTeX | Altmetric | Tags: biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways @article{wicker2010predicting, title = {Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach}, author = {J\"{o}rg Wicker and Kathrin Fenner and Lynda Ellis and Larry Wackett and Stefan Kramer}, url = {http://bioinformatics.oxfordjournals.org/content/26/6/814.full}, doi = {10.1093/bioinformatics/btq024}, year = {2010}, date = {2010-01-01}, journal = {Bioinformatics}, volume = {26}, number = {6}, pages = {814-821}, publisher = {Oxford University Press}, abstract = {Motivation: Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this article, we propose a hybrid knowledge- and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. As the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice.Results: Results from leave-one-out cross-validation show that a recall and precision of ∼0.8 can be achieved for a subset of 13 transformation rules. Therefore, it is possible to optimize precision without compromising recall. We are currently integrating the results into an experimental version of the UM-PPS server.Availability: The program is freely available on the web at http://wwwkramer.in.tum.de/research/applications/biodegradation/data.Contact: kramer@in.tum.de}, keywords = {biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways}, pubstate = {published}, tppubtype = {article} } Motivation: Current methods for the prediction of biodegradation products and pathways of organic environmental pollutants either do not take into account domain knowledge or do not provide probability estimates. In this article, we propose a hybrid knowledge- and machine learning-based approach to overcome these limitations in the context of the University of Minnesota Pathway Prediction System (UM-PPS). The proposed solution performs relative reasoning in a machine learning framework, and obtains one probability estimate for each biotransformation rule of the system. As the application of a rule then depends on a threshold for the probability estimate, the trade-off between recall (sensitivity) and precision (selectivity) can be addressed and leveraged in practice.Results: Results from leave-one-out cross-validation show that a recall and precision of ∼0.8 can be achieved for a subset of 13 transformation rules. Therefore, it is possible to optimize precision without compromising recall. We are currently integrating the results into an experimental version of the UM-PPS server.Availability: The program is freely available on the web at http://wwwkramer.in.tum.de/research/applications/biodegradation/data.Contact: kramer@in.tum.de | ||
| 25. | Wicker, Jörg; Richter, Lothar; Kramer, Stefan SINDBAD and SiQL: Overview, Applications and Future Developments Incollection In: Džeroski, Sašo; Goethals, Bart; Panov, Panče (Ed.): Inductive Databases and Constraint-Based Data Mining, pp. 289-309, Springer New York, 2010, ISBN: 978-1-4419-7737-3. Abstract | Links | BibTeX | Altmetric | Tags: data mining, inductive databases, machine learning, query languages @incollection{wicker2010sindbad, title = {SINDBAD and SiQL: Overview, Applications and Future Developments}, author = {J\"{o}rg Wicker and Lothar Richter and Stefan Kramer}, editor = {Sa\v{s}o D\v{z}eroski and Bart Goethals and Pan\v{c}e Panov}, url = {http://dx.doi.org/10.1007/978-1-4419-7738-0_12}, doi = {10.1007/978-1-4419-7738-0_12}, isbn = {978-1-4419-7737-3}, year = {2010}, date = {2010-01-01}, booktitle = {Inductive Databases and Constraint-Based Data Mining}, pages = {289-309}, publisher = {Springer New York}, abstract = {The chapter gives an overview of the current state of the Sindbad system and planned extensions. Following an introduction to the system and its query language SiQL, we present application scenarios from the areas of gene expression/regulation and small molecules. Next, we describe a web service interface to Sindbad that enables new possibilities for inductive databases (distributing tasks over multiple servers, language and platform independence, …). Finally, we discuss future plans for the system, in particular, to make the system more ‘declarative’ by the use of signatures, to integrate the useful concept of mining views into the system, and to support specific pattern domains like graphs and strings.}, keywords = {data mining, inductive databases, machine learning, query languages}, pubstate = {published}, tppubtype = {incollection} } The chapter gives an overview of the current state of the Sindbad system and planned extensions. Following an introduction to the system and its query language SiQL, we present application scenarios from the areas of gene expression/regulation and small molecules. Next, we describe a web service interface to Sindbad that enables new possibilities for inductive databases (distributing tasks over multiple servers, language and platform independence, …). Finally, we discuss future plans for the system, in particular, to make the system more ‘declarative’ by the use of signatures, to integrate the useful concept of mining views into the system, and to support specific pattern domains like graphs and strings. | ||
2008 |
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| 26. | Wicker, Jörg; Richter, Lothar; Kessler, Kristina; Kramer, Stefan SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model Inproceedings In: Daelemans, Walter; Goethals, Bart; Morik, Katharina (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 690-694, Springer Berlin Heidelberg, 2008, ISBN: 978-3-540-87480-5. Abstract | Links | BibTeX | Altmetric | Tags: data mining, inductive databases, machine learning, query languages @inproceedings{wicker2008sindbad, title = {SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model}, author = {J\"{o}rg Wicker and Lothar Richter and Kristina Kessler and Stefan Kramer}, editor = {Walter Daelemans and Bart Goethals and Katharina Morik}, url = {http://dx.doi.org/10.1007/978-3-540-87481-2_48}, doi = {10.1007/978-3-540-87481-2_48}, isbn = {978-3-540-87480-5}, year = {2008}, date = {2008-01-01}, booktitle = {Machine Learning and Knowledge Discovery in Databases}, volume = {5212}, pages = {690-694}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, abstract = {In this demonstration, we will present the concepts and an implementation of an inductive database \textendash as proposed by Imielinski and Mannila \textendash in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.}, keywords = {data mining, inductive databases, machine learning, query languages}, pubstate = {published}, tppubtype = {inproceedings} } In this demonstration, we will present the concepts and an implementation of an inductive database – as proposed by Imielinski and Mannila – in the relational model. The goal is to support all steps of the knowledge discovery process on the basis of queries to a database system. The query language SiQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and software architecture significantly. The prototype is applied to three different data sets: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules. | ||
| 27. | Richter, Lothar; Wicker, Jörg; Kessler, Kristina; Kramer, Stefan An Inductive Database and Query Language in the Relational Model Inproceedings In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology, pp. 740–744, ACM, 2008, ISBN: 978-1-59593-926-5. Abstract | Links | BibTeX | Altmetric | Tags: data mining, inductive databases, machine learning, query languages @inproceedings{richter2008inductive, title = {An Inductive Database and Query Language in the Relational Model}, author = {Lothar Richter and J\"{o}rg Wicker and Kristina Kessler and Stefan Kramer}, url = {https://wicker.nz/nwp-acm/authorize.php?id=N10033 http://doi.acm.org/10.1145/1353343.1353440}, doi = {10.1145/1353343.1353440}, isbn = {978-1-59593-926-5}, year = {2008}, date = {2008-01-01}, booktitle = {Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology}, pages = {740--744}, publisher = {ACM}, series = {EDBT '08}, abstract = {In the demonstration, we will present the concepts and an implementation of an inductive database -- as proposed by Imielinski and Mannila -- in the relational model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query language SIQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. Key concepts of this system, among others, are the closure of operators and distances between objects. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and the software architecture significantly. The prototype is applied to three different applications: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules.}, keywords = {data mining, inductive databases, machine learning, query languages}, pubstate = {published}, tppubtype = {inproceedings} } In the demonstration, we will present the concepts and an implementation of an inductive database -- as proposed by Imielinski and Mannila -- in the relational model. The goal is to support all steps of the knowledge discovery process, from pre-processing via data mining to post-processing, on the basis of queries to a database system. The query language SIQL (structured inductive query language), an SQL extension, offers query primitives for feature selection, discretization, pattern mining, clustering, instance-based learning and rule induction. A prototype system processing such queries was implemented as part of the SINDBAD (structured inductive database development) project. Key concepts of this system, among others, are the closure of operators and distances between objects. To support the analysis of multi-relational data, we incorporated multi-relational distance measures based on set distances and recursive descent. The inclusion of rule-based classification models made it necessary to extend the data model and the software architecture significantly. The prototype is applied to three different applications: gene expression analysis, gene regulation prediction and structure-activity relationships (SARs) of small molecules. | ||
| 28. | Wicker, Jörg; Brosdau, Christoph; Richter, Lothar; Kramer, Stefan SINDBAD SAILS: A Service Architecture for Inductive Learning Schemes Inproceedings In: Proceedings of the First Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery, 2008. Abstract | Links | BibTeX | Tags: data mining, inductive databases, machine learning, query languages @inproceedings{wicker2008sindbadsails, title = {SINDBAD SAILS: A Service Architecture for Inductive Learning Schemes}, author = {J\"{o}rg Wicker and Christoph Brosdau and Lothar Richter and Stefan Kramer}, url = {http://www.ecmlpkdd2008.org/files/pdf/workshops/sokd/2.pdf}, year = {2008}, date = {2008-01-01}, booktitle = {Proceedings of the First Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery}, abstract = {The paper presents SINDBAD SAILS (Service Architecture for Inductive Learning Schemes), a Web Service interface to the inductive database SINDBAD. To the best of our knowledge, it is the first time a Web Service interface is provided for an inductive database. The combination of service-oriented architectures and inductive databases is particularly useful, as it enables distributed data mining without the need to install specialized data mining or machine learning software. Moreover, inductive queries can easily be used in almost any kind of programming language. The paper discusses the underlying concepts and explains a sample program making use of SINDBAD SAILS.}, keywords = {data mining, inductive databases, machine learning, query languages}, pubstate = {published}, tppubtype = {inproceedings} } The paper presents SINDBAD SAILS (Service Architecture for Inductive Learning Schemes), a Web Service interface to the inductive database SINDBAD. To the best of our knowledge, it is the first time a Web Service interface is provided for an inductive database. The combination of service-oriented architectures and inductive databases is particularly useful, as it enables distributed data mining without the need to install specialized data mining or machine learning software. Moreover, inductive queries can easily be used in almost any kind of programming language. The paper discusses the underlying concepts and explains a sample program making use of SINDBAD SAILS. | ||
| 29. | Wicker, Jörg; Fenner, Kathrin; Ellis, Lynda; Wackett, Larry; Kramer, Stefan Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction Inproceedings In: Bridewell, Will; Calders, Toon; de Medeiros, Ana Karla; Kramer, Stefan; Pechenizkiy, Mykola; Todorovski, Ljupco (Ed.): Proceedings of the Second International Workshop on the Induction of Process Models at ECML PKDD 2008, 2008. Links | BibTeX | Tags: biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways @inproceedings{wicker2008machine, title = {Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction}, author = {J\"{o}rg Wicker and Kathrin Fenner and Lynda Ellis and Larry Wackett and Stefan Kramer}, editor = {Will Bridewell and Toon Calders and Ana Karla de Medeiros and Stefan Kramer and Mykola Pechenizkiy and Ljupco Todorovski}, url = {http://www.ecmlpkdd2008.org/files/pdf/workshops/ipm/9.pdf}, year = {2008}, date = {2008-01-01}, booktitle = {Proceedings of the Second International Workshop on the Induction of Process Models at ECML PKDD 2008}, keywords = {biodegradation, cheminformatics, computational sustainability, enviPath, machine learning, metabolic pathways}, pubstate = {published}, tppubtype = {inproceedings} } | ||
2006 |
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| 30. | Kramer, Stefan; Aufschild, Volker; Hapfelmeier, Andreas; Jarasch, Alexander; Kessler, Kristina; Reckow, Stefan; Wicker, Jörg; Richter, Lothar Inductive Databases in the Relational Model: The Data as the Bridge Inproceedings In: Bonchi, Francesco; ç, Jean-Fran (Ed.): Knowledge Discovery in Inductive Databases, pp. 124-138, Springer Berlin Heidelberg, 2006, ISBN: 978-3-540-33292-3. Abstract | Links | BibTeX | Altmetric | Tags: data mining, inductive databases, machine learning, query languages @inproceedings{kramer2006inductive, title = {Inductive Databases in the Relational Model: The Data as the Bridge}, author = {Stefan Kramer and Volker Aufschild and Andreas Hapfelmeier and Alexander Jarasch and Kristina Kessler and Stefan Reckow and J\"{o}rg Wicker and Lothar Richter}, editor = {Francesco Bonchi and Jean-Fran{\c{c}}ois Boulicaut}, url = {http://dx.doi.org/10.1007/11733492_8}, doi = {10.1007/11733492_8}, isbn = {978-3-540-33292-3}, year = {2006}, date = {2006-01-01}, booktitle = {Knowledge Discovery in Inductive Databases}, volume = {3933}, pages = {124-138}, publisher = {Springer Berlin Heidelberg}, series = {Lecture Notes in Computer Science}, abstract = {We present a new and comprehensive approach to inductive databases in the relational model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community.}, keywords = {data mining, inductive databases, machine learning, query languages}, pubstate = {published}, tppubtype = {inproceedings} } We present a new and comprehensive approach to inductive databases in the relational model. The main contribution is a new inductive query language extending SQL, with the goal of supporting the whole knowledge discovery process, from pre-processing via data mining to post-processing. A prototype system supporting the query language was developed in the SINDBAD (structured inductive database development) project. Setting aside models and focusing on distance-based and instance-based methods, closure can easily be achieved. An example scenario from the area of gene expression data analysis demonstrates the power and simplicity of the concept. We hope that this preliminary work will help to bring the fundamental issues, such as the integration of various pattern domains and data mining techniques, to the attention of the inductive database community. | ||
2021 |
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| 1. | A comprehensive comparison of molecular feature representations for use in predictive modeling Journal Article In: Computers in Biology and Medicine, 130 , pp. 104197, 2021, ISSN: 0010-4825. | ||
2020 |
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| 2. | Balancing Utility and Fairness against Privacy in Medical Data Inproceedings In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1226-1233, IEEE, 2020. | ||
| 3. | Your Best Guess When You Know Nothing: Identification and Mitigation of Selection Bias Inproceedings Forthcoming In: 2020 IEEE International Conference on Data Mining (ICDM), IEEE, Forthcoming. | ||
| 4. | Feature Engineering for a Seismic Loss Prediction Model using Machine Learning, Christchurch Experience Inproceedings Forthcoming In: 17th World Conference on Earthquake Engineering, Forthcoming. | ||
| 5. | A machine learning damage prediction model for the 2017 Puebla-Morelos, Mexico, earthquake Journal Article In: Earthquake Spectra, 36 (2), pp. 314-339, 2020. | ||
2019 |
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| 6. | XOR-based Boolean Matrix Decomposition Inproceedings In: Wang, Jianyong; Shim, Kyuseok; Wu, Xindong (Ed.): 2019 IEEE International Conference on Data Mining (ICDM), pp. 638-647, IEEE, 2019, ISBN: 978-1-7281-4604-1. | ||
| 7. | A machine learning approach to quantifying the specificity of color-emotion associations and their cultural differences Journal Article In: Royal Society Open Science, 6 (9), pp. 190741, 2019. | ||
| 8. | Data integration for the development of a seismic loss prediction model for residential buildings in New Zealand Inproceedings In: Cellier, Peggy; Driessens, Kurt (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 88-100, Springer International Publishing, Cham, 2019, ISBN: 978-3-030-43887-6. | ||
| 9. | What can we learn from the air chemistry of crowds? Inproceedings In: Hansel, Armin; Dunkl, Jürgen (Ed.): 8th International Conference on Proton Transfer Reaction Mass Spectrometry and its Applications, pp. 121-123, Innsbruck University Press, Innsbruck, 2019. | ||
2018 |
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| 10. | Proof of concept study: Testing human volatile organic compounds as tools for age classification of films Journal Article In: PLOS One, 13 (10), pp. 1-14, 2018. | ||
2017 |
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| 11. | The Best Privacy Defense is a Good Privacy Offense: Obfuscating a Search Engine User's Profile Journal Article In: Data Mining and Knowledge Discovery, 31 (5), pp. 1419-1443, 2017, ISSN: 1573-756X. | ||
| 12. | Eawag-Soil in enviPath: a new resource for exploring regulatory pesticide soil biodegradation pathways and half-life data Journal Article In: Environmental Science: Process & Impact, 2017. | ||
2016 |
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| 13. | A Hybrid Machine Learning and Knowledge Based Approach to Limit Combinatorial Explosion in Biodegradation Prediction Incollection In: Lässig, Jörg; Kersting, Kristian; Morik, Katharina (Ed.): Computational Sustainability, pp. 75-97, Springer International Publishing, Cham, 2016, ISBN: 978-3-319-31858-5. | ||
| 14. | A Nonlinear Label Compression and Transformation Method for Multi-Label Classification using Autoencoders Inproceedings In: Bailey, James; Khan, Latifur; Washio, Takashi; Dobbie, Gill; Huang, Zhexue Joshua; Wang, Ruili (Ed.): The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 328-340, Springer International Publishing, Switzerland, 2016, ISBN: 978-3-319-31753-3. | ||
| 15. | enviPath - The Environmental Contaminant Biotransformation Pathway Resource Journal Article In: Nucleic Acid Research, 44 (D1), pp. D502-D508, 2016. | ||
| 16. | Trading Off Accuracy for Efficiency by Randomized Greedy Warping Inproceedings In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 883-890, ACM, New York, NY, USA, 2016, ISBN: 978-1-4503-3739-7. | ||
| 17. | Cinema audiences reproducibly vary the chemical composition of air during films, by broadcasting scene specific emissions on breath Journal Article In: Scientific Reports, 6 , 2016. | ||
2015 |
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| 18. | Cinema Data Mining: The Smell of Fear Inproceedings In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235-1304, ACM ACM, New York, NY, USA, 2015, ISBN: 978-1-4503-3664-2. | ||
| 19. | Scavenger - A Framework for the Efficient Evaluation of Dynamic and Modular Algorithms Inproceedings In: Bifet, Albert; May, Michael; Zadrozny, Bianca; Gavalda, Ricard; Pedreschi, Dino; Cardoso, Jaime; Spiliopoulou, Myra (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 325-328, Springer International Publishing, 2015, ISBN: 978-3-319-23460-1. | ||
2014 |
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| 20. | BMaD -- A Boolean Matrix Decomposition Framework Inproceedings In: Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke; Meo, Rosa (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 481-484, Springer Berlin Heidelberg, 2014, ISBN: 978-3-662-44844-1. | ||
2013 |
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| 21. | Large Classifier Systems in Bio- and Cheminformatics PhD Thesis Technische Universität München, 2013. | ||
2012 |
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| 22. | Multi-label Classification Using Boolean Matrix Decomposition Inproceedings In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 179–186, ACM, 2012, ISBN: 978-1-4503-0857-1. | ||
2010 |
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| 23. | Collaborative development of predictive toxicology applications Journal Article In: Journal of Cheminformatics, 2 (1), pp. 7, 2010, ISSN: 1758-2946. | ||
| 24. | Predicting biodegradation products and pathways: a hybrid knowledge- and machine learning-based approach Journal Article In: Bioinformatics, 26 (6), pp. 814-821, 2010. | ||
| 25. | SINDBAD and SiQL: Overview, Applications and Future Developments Incollection In: Džeroski, Sašo; Goethals, Bart; Panov, Panče (Ed.): Inductive Databases and Constraint-Based Data Mining, pp. 289-309, Springer New York, 2010, ISBN: 978-1-4419-7737-3. | ||
2008 |
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| 26. | SINDBAD and SiQL: An Inductive Database and Query Language in the Relational Model Inproceedings In: Daelemans, Walter; Goethals, Bart; Morik, Katharina (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 690-694, Springer Berlin Heidelberg, 2008, ISBN: 978-3-540-87480-5. | ||
| 27. | An Inductive Database and Query Language in the Relational Model Inproceedings In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology, pp. 740–744, ACM, 2008, ISBN: 978-1-59593-926-5. | ||
| 28. | SINDBAD SAILS: A Service Architecture for Inductive Learning Schemes Inproceedings In: Proceedings of the First Workshop on Third Generation Data Mining: Towards Service-Oriented Knowledge Discovery, 2008. | ||
| 29. | Machine Learning and Data Mining Approaches to Biodegradation Pathway Prediction Inproceedings In: Bridewell, Will; Calders, Toon; de Medeiros, Ana Karla; Kramer, Stefan; Pechenizkiy, Mykola; Todorovski, Ljupco (Ed.): Proceedings of the Second International Workshop on the Induction of Process Models at ECML PKDD 2008, 2008. | ||
2006 |
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| 30. | Inductive Databases in the Relational Model: The Data as the Bridge Inproceedings In: Bonchi, Francesco; ç, Jean-Fran (Ed.): Knowledge Discovery in Inductive Databases, pp. 124-138, Springer Berlin Heidelberg, 2006, ISBN: 978-3-540-33292-3. | ||