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Issue No. 03 - July-September (2010 vol. 7)
ISSN: 1545-5963
pp: 563-571
Arthur Zimek , Ludwig-Maximilians-Universitaet Muenchen and Forschungseinheit fuer Datenbanksysteme, Muenchen
Fabian Buchwald , Technische Universitaet Muenchen, Muenchen
Eibe Frank , University of Waikato, Hamilton
Stefan Kramer , Technische Universitaet Muenchen, Muenchen
Automatic classification of proteins using machine learning is an important problem that has received significant attention in the literature. One feature of this problem is that expert-defined hierarchies of protein classes exist and can potentially be exploited to improve classification performance. In this article, we investigate empirically whether this is the case for two such hierarchies. We compare multiclass classification techniques that exploit the information in those class hierarchies and those that do not, using logistic regression, decision trees, bagged decision trees, and support vector machines as the underlying base learners. In particular, we compare hierarchical and flat variants of ensembles of nested dichotomies. The latter have been shown to deliver strong classification performance in multiclass settings. We present experimental results for synthetic, fold recognition, enzyme classification, and remote homology detection data. Our results show that exploiting the class hierarchy improves performance on the synthetic data but not in the case of the protein classification problems. Based on this, we recommend that strong flat multiclass methods be used as a baseline to establish the benefit of exploiting class hierarchies in this area.
Protein classification, hierarchical classification, multiclass classification.

F. Buchwald, S. Kramer, E. Frank and A. Zimek, "A Study of Hierarchical and Flat Classification of Proteins," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. , pp. 563-571, 2008.
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