SVM-RFE with relevancy and redundancy criteria for gene selection

Authors:
Piyushkumar A. Mundra;Jagath C. Rajapakse
Affiliations:
Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore;Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore and Singapore-MIT Alliance, Singapore
Venue:
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Year:
2007

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Abstract

This paper introduces a novel gene selection method incorporating mutual information in the support vector machine recursive feature elimination (SVM-RFE). We incorporate an additional term of mutual information based minimum redundancy maximum relevancy criteria along with feature weight calculated by SVM algorithm. We tested proposed method on colon cancer and leukemia cancer gene expression dataset. The results show that the proposed method performs better than the original SVM-RFE method. The selected gene subset has better classification accuracy and better generalization capability.