Abstract: Gene selection is to select the most informative genes from the whole gene set,which is a key step of the discriminant analysis of microarray data．Rough set theory is an efficient mathematical tool for further reducing redundancy．The main limitation of traditional rough set theory is the lack of effective methods for dealing with real-valued data．However,gene expression data sets are always continuous．This has been addressed by employing discretization methods,which may result in information loss．This paper investigates one approach combining feature ranking together with features selection based on tolerance rough set theory．Moreover,this paper explores the other method which can utilize the information contained within the boundary region to improve classification accuracy in gene expression data．Compared with gene selection algorithm based on rough set theory,the proposed methods are more effective for selecting high discriminative genes in cancer classification task.

Key words: Rough set theory,Tolerance relation,Gene selection,Gene expression data,Cancer classification