Gene Selection with Tolerance Rough Set Theory from Gene Expression Data
基于相容关系的基因选择方法

Efficient gene selection is a key procedure of the discriminant analysis of microarray data. Rough set theory is an efficient tool for further reducing redundancy. One limitation of rough set theory is the lack of effective methods for processing real-valued data. However, gene expression data sets are always continuous. Discretization methods can result in information loss. hhis paper investigated an approach combining feature ranking together with features selection based on tolerance rough set theory. To evaluate the performance of the proposed approach, we applied it to two benchmark gene expression data sets and compared our results with those obtained by conventional method. Experimental resups illustrate that our algorithm is more effective for selecting high discriminative genes in cancer classification task.