Rough set aided gene selection for cancer classification

Gene selection is of vital importance in molecular classification of cancer using high-dimensional gene expression data. Because of the distinct characteristics inherent to specific cancerous gene expression profiles, developing flexible and robust feature selection methods is extremely crucial. A new method, Supervised CFS-Quick Reduct algorithm by combining Correlation based Feature Selection (CFS) and Rough Sets attribute reduction together for gene selection from gene expression data is proposed. Correlation based Feature Selection 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. The novel method improves the efficiency and decreases the complexity of the classical algorithm. Extensive experiments are conducted on two public multi-class gene expression datasets and the experimental results 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.