A hybrid discriminative/generative approach to protein fold recognition

Authors:
WiesŁaw Chmielnicki;Katarzyna Stapor
Affiliations:
Jagiellonian University, Faculty of Physics, Astronomy and Applied Computer Science, ul. Reymonta 4, 30-059 Kraków, Poland;Silesian University of Technology, Institute of Computer Science, ul. Akademicka 16, 44-100 Gliwice, Poland
Venue:
Neurocomputing
Year:
2012

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Abstract

There are two standard approaches to the classification task: generative, which use training data to estimate a probability model for each class, and discriminative, which try to construct flexible decision boundaries between the classes. An ideal classifier should combine these two approaches. In this paper a classifier combining the well-known support vector machine (SVM) classifier with regularized discriminant analysis (RDA) classifier is presented. The hybrid classifier is used for protein structure prediction which is one of the most important goals pursued by bioinformatics. The obtained results are promising, the hybrid classifier achieves better result than the SVM or RDA classifiers alone. The proposed method achieves higher recognition ratio than other methods described in the literature.