adversarial learning atmospheric chemistry autoencoders biodegradation Boolean matrix decomposition breath analysis causality cheminformatics cinema data mining computational sustainability data mining earthquakes emotional response analysis enviPath framework inductive databases label compression large-scale linked data machine learning metabolic pathways movie analysis multi-label classification query languages reinforcement learning REST smell of fear sof time series toxicity

## 2013 |
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1. | 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 = {Jörg Wicker}, url = {http://mediatum.ub.tum.de/node?id=1165858}, year = {2013}, date = {2013-01-01}, school = {Technische Universität München}, 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. |