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.

### 2017

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

@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 = {},

pubstate = {published},

tppubtype = {article}

}

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

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

@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 = {},

pubstate = {published},

tppubtype = {incollection}

}

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

@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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}

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

@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 = {},

pubstate = {published},

tppubtype = {article}

}

### 2013

Wicker, Jörg

Large Classifier Systems in Bio- and Cheminformatics PhD Thesis

Technische Universität München, 2013.

@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 = {},

pubstate = {published},

tppubtype = {phdthesis}

}

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

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

@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 = {},

pubstate = {published},

tppubtype = {inproceedings}

}