Fuzzy-rough attribute reduction via mutual information with an application to cancer classification

Establishing a classification model for cancer recognition based on DNA microarrays is useful for cancer diagnosis. Feature selection is a key step to perform cancer classification with DNA microarrays, for there is a large number of genes from which to predict classes and a relatively small number of samples. Automatic methods must be developed for extracting relevant genes which are essential for classification. This paper proposes a novel approach for reducing data redundancy based on fuzzy rough set theory and information theory. A mutual information-based algorithm for attribute reduction is suggested. The method is applied to the problem of gene selection for cancer classification. Experimental results show that the algorithm is more effective than conventional rough sets based approaches.