Feature Selection and Classification for Gene Expression Data using Evolutionary Computation

An evolutionary rough feature selection algorithm is proposed for classifying gene expression patterns. Since the data typically consist of a large number of redundant features, an initial redundancy reduction of the attributes is done to enable faster convergence. Rough set theory is employed to generate the distinction table that enable PSO to find reducts, which represent the minimal sets of non-redundant features capable of discerning between all objects. The effectiveness of the algorithm is demonstrated on three benchmark cancer datasets viz. Colon, Lymphoma and Leukemia using MOGA.