Gene subset selection in kernel-induced feature space

Authors:
Satoshi Niijima;Satoru Kuhara
Affiliations:
Department of Bioinformatics, Graduate School of Systems Life Sciences, Kyushu University, Hakozaki 6-10-1, Higashi-ku, Fukuoka 812-8581, Japan;Faculty of Agriculture, Kyushu University, Hakozaki 6-10-1, Higashi-ku, Fukuoka 812-8581, Japan
Venue:
Pattern Recognition Letters
Year:
2006

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Abstract

This paper proposes a new filter approach to gene subset selection for kernel-based classifiers. We derive kernel forms of several well-known class separability criteria, and gene subset selection based on the kernelized criteria is applied to microarray cancer classification problems. The performance of our proposed strategy is compared in experiments with those of the conventional filter approach as well as gene ranking methods.