Gene Selection with Rough Sets for Cancer Classification

A new method combining correlation based clustering and rough sets attribute reduction together for gene selection from gene expression data is proposed. Correlation based clustering is used as a filter to eliminate the redundant attributes, then the minimal reduct of the filtered attribute set is reduced by rough sets . Three different classification algorithms are employed to evaluate the performance of this novel method. High classification accuracies achieved on two public gene expression data sets show that this method is successful for selecting high discriminative genes for classification task. The experimental results indicate that rough sets based method has the potential to become a useful tool in bioinformatics.