Hybrid huberized support vector machines for microarray classification and gene selection

Abstract

Motivation: The standard L₂-norm support vector machine (SVM) is a widely used tool for microarray classification. Previous studies have demonstrated its superior performance in terms of classification accuracy. However, a major limitation of the SVM is that it cannot automatically select relevant genes for the classification. The L₁-norm SVM is a variant of the standard L₂-norm SVM, that constrains the L₁-norm of the fitted coefficients. Due to the singularity of the L₁-norm, the L₁-norm SVM has the property of automatically selecting relevant genes. On the other hand, the L₁-norm SVM has two drawbacks: (1) the number of selected genes is upper bounded by the size of the training data; (2) when there are several highly correlated genes, the L₁-norm SVM tends to pick only a few of them, and remove the rest.