One-versus-one and one-versus-all multiclass SVM-RFE for gene selection in cancer classification

Authors:
Kai-Bo Duan;Jagath C. Rajapakse;Minh N. Nguyen
Affiliations:
BioInformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore;BioInformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore and Singapore-MIT Alliance, Singapore;BioInformatics Research Centre, School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Year:
2007

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Abstract

We propose a feature selection method for multiclass classification. The proposed method selects features in backward elimination and computes feature ranking scores at each step from analysis of weight vectors of multiple two-class linear Support Vector Machine classifiers from one-versus-one or one-versus-all decomposition of a multi-class classification problem.We evaluated the proposed method on three gene expression datasets for multiclass cancer classification. For comparison, one filtering feature selection method was included in the numerical study. The study demonstrates the effectiveness of the proposed method in selecting a compact set of genes to ensure a good classification accuracy.