Title: Using classifier fusion techniques for protein secondary structure prediction

Authors: Majid Kazemian, Behzad Moshiri, Vasile Palade, Hamid Nikbakht, Caro Lucas

Addresses: Control and Intelligent Processing Center, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. ' Control and Intelligent Processing Center, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. ' Computing Laboratory, Oxford University, Parks Road, Oxford, OX1 3QD, UK. ' Laboratory of Biophysics and Molecular Biology, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran. ' Control and Intelligent Processing Center, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

Abstract: Classifier fusion techniques are gaining more popularity for their capability of improving the accuracy achieved by individual classifiers. A common approach is to combine the classifiers| outcome using simple methods, such as majority voting. In this paper, we build a meta-classifier by fusing some already well-known classifiers for protein structure prediction. Each individual classifier outputs a unique structure for every input residue. We have used the confusion matrix of each protein secondary structure classifier, which is representative of classifiers| expertness, as a general reusable pattern for converting its simple class-label assignment to class-preference score. The results obtained using several classifier fusion operators have been compared, on some standard datasets from the EVA server, with simple majority voting and with the results provided by the individual classifiers. The comparative analysis showed that the Choquet fuzzy integral operator had the highest improvement with respect to accuracy, multi-class sensitivity and specificity criteria over both the best performing individual classifier and the other fusion operators, while all of the classifier fusion techniques yielded some improvements too.

Keywords: protein secondary structure prediction; classifier fusion; Choquet fuzzy integral operator; metaclassifiers; confusion matrix; multi-class sensitivity; protein structure; bioinformatics.

DOI: 10.1504/IJCIBSB.2010.038225

International Journal of Computational Intelligence in Bioinformatics and Systems Biology, 2010 Vol.1 No.4, pp.418 - 434

Published online: 23 Jan 2011 *

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