A Two-Stage Gene Selection Algorithm by Combining ReliefF and mRMR

Abstract—Gene expression data usually contains a large number of genes, but a small number of samples. Feature selection for gene expression data aims at finding a set of genes that best discriminate biological samples of different types. In this paper, we present a two-stage selection algorithm by combining ReliefF and mRMR: In the first stage, ReliefF is applied to find a candidate gene set; In the second stage, mRMR method is applied to directly and explicitly reduce redundancy for selecting a compact yet effective gene subset from the candidate set. We also perform comprehensive experiments to compare the mRMR-ReliefF selection algorithm with ReliefF, mRMR and other feature selection methods using two classifiers as SVM and Naive Bayes, on seven different datasets. The experimental results show that the mRMR-ReliefF gene selection algorithm is very effective.

• Type: Conference
• Year: 2007
• Where: BIBE
• Authors: Yi Zhang, Chris H. Q. Ding, Tao Li
• Keywords: BIBE 2007, Bioinformatics, Expression Data, Feature Selection, Selection Algorithm