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

Authors:
Chia Huey Ooi;Madhu Chetty;Shyh Wei Teng
Affiliations:
Gippsland School of Information Technology, Monash University, Churchill, VIC, Australia;Gippsland School of Information Technology, Monash University, Churchill, VIC, Australia;Gippsland School of Information Technology, Monash University, Churchill, VIC, Australia
Venue:
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
Year:
2006

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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.