Improving MSVM-RFE for Multiclass Gene Selection

Along with the advent of DNA microarray technology, gene expression profiling has been widely used to study molecular signatures of many diseases and to develop molecular diagnostics for disease prediction. In class prediction problems using expression data, gene selection is essential to improve the prediction accuracy and to identify informative genes for a disease. In this paper we improve the multi-class support vector machine-recursive feature elimination (MSVM-RFE) by combining minimum redundancy maximum relevancy (mRMR) criterion and introducing the kernel. The result is the better performance with a smaller number of irredundant genes for multi-class datasets.