Applying PSO in Finding Useful Features

Abstract

In data mining and knowledge discovery, the curse of dimensionality is a damning factor for numerous potentially powerful machine learning techniques, while rough set theory can be employed to reduce the dimensionality of datasets as a preprocessing step. For rough set based methods, finding reducts is an essential step, yet it is of high complexity. In this paper, based on particle swarm optimization(PSO) which is an optimization algorithm inspired by social behavior of flocks of birds when they are searching for food, a novel method is proposed for finding useful features instead of reducts in rough set theory. Subsequent experiments on UCI show that this method performs well on whole convergence, and can retrieve useful subsets effectively while retaining attributes of high importance as possible.