Hybridization of rough set and differential evolution technique for optimal features selection

Abstract

Dimensionality reduction of a feature set refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. But it can be made to be optimal using different optimization techniques. This paper proposes a new feature selection method based on Rough set theory hybrid with Differential Evolution (DE) try to improve this. We call this method as RoughDE. The proposed method is experimentally compared with other hybrid Rough Set methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). © 2012 Springer-Verlag GmbH Berlin Heidelberg.