A Novel Recursive Feature Subset Selection Algorithm

Univariate filter methods, which rank single genes according to how well they each separate the classes, are widely used for gene ranking in the field of microarray analysis of gene expression datasets. These methods rank all of the genes by considering all of the samples; however some of these samples may never be classified correctly by adding new genes and these methods keep adding redundant genes covering only some parts of the space and finally the returned subset of genes may never cover the space perfectly. In this paper we introduce a new gene subset selection approach which aims to add genes covering the space which has not been covered by already selected genes in a recursive fashion. Our approach leads to significant improvement on many different benchmark datasets.