OVA scheme vs. single machine approach in feature selection for microarray datasets

Abstract

The large number of genes in microarray data makes feature selection techniques more crucial than ever. From rank-based filter techniques to classifier-based wrapper techniques, many studies have devised their own feature selection techniques for microarray datasets. By combining the OVA (one-vs.-all) approach and differential prioritization in our feature selection technique, we ensure that class-specific relevant features are selected while guarding against redundancy in predictor set at the same time. In this paper we present the OVA version of our differential prioritization-based feature selection technique and demonstrate how it works better than the original SMA (single machine approach) version. © Springer-Verlag Berlin Heidelberg 2006.