Minimum redundancy maximum relevance and VNS based gene selection for cancer classification in high-dimensional data

Abstract

DNA microarray is a technique for measuring simultaneously the expression levels of a huge number of genes, these levels have a significant impact on cancer classification tasks. In DNA datasets, the number of genes exceeds the number of samples that make the presence of irrelevant or redundant genes possible. In this paper, two hybrid multivariate filters for gene selection, named VNSMI and VNSCor, are presented. These methods surpass the univariate filters by considering the possible interaction between genes through the search for a subset of genes that contains the minimum redundancy and the maximum relevance (MRMR). In the first stage of our approaches, we use a univariate filter by selecting the best-ranked genes. Then, we apply the variable neighbourhood search (VNS) metaheuristic coupled with an innovative stochastic local search (SLS) algorithm to find the final subset of genes. The experiments performed show that the proposed approaches are feasible and effective.