Support vectors based correlation coefficient for gene and sample selection in cancer classification

Correlation is a very widely used filter criterion for gene selection in cancer classification. However, it uses all the training samples in ranking, which may not be equally important for the classification. Using support vectors, we demonstrate that classical correlation coefficient based gene selection is biased because of the sample points away from classification margin. To remove such bias, we use only the support vectors for computation of correlation coefficient and propose a backward elimination based SVcc-RFE algorithm. The proposed method is tested on several benchmark cancer gene expression datasets and the results show improvement in classification performance compared to other state-of-the-art methods.