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In statistics and machine learning, feature selection is the process ofpicking a subset of relevant attributes for utilizing in a predictive model.Recently, rough set-based feature selection techniques, that employ featuredependency to perform selection process, have been drawn attention.Classification of tumors based on gene expression is utilized to diagnoseproper treatment and prognosis of the disease in bioinformatics applications.Microarray gene expression data includes superfluous feature genes of highdimensionality and smaller training instances. Since exact supervisedclassification of gene expression instances in such high-dimensional problemsis very complex, the selection of appropriate genes is a crucial task for tumorclassification. In this study, we present a new technique for gene selectionusing a discernibility matrix of fuzzy-rough sets. The proposed technique takesinto account the similarity of those instances that have the same and differentclass labels to improve the gene selection results, while the state-of-the artprevious approaches only address the similarity of instances with differentclass labels. To meet that requirement, we extend the Johnson reducer techniqueinto the fuzzy case. Experimental results demonstrate that this techniqueprovides better efficiency compared to the state-of-the-art approaches.

A New Gene Selection Algorithm using Fuzzy-Rough Set Theory for Tumor Classification