A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy

A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy

Feature selection has been widely used in data mining and machine learning tasks to make a model with a small number of features which improves the classifier’s accuracy. In this paper, a novel hybrid feature selection algorithm based on particle swarm optimization is proposed. The proposed method called HPSO-LS uses a local search strategy which is embedded in the particle swarm optimization to select the less correlated and salient feature subset. The goal of the local search technique is to guide the search process of the particle swarm optimization to select distinct features by considering their correlation information. Moreover, the proposed method utilizes a subset size determination scheme to select a subset of features with reduced size. The performance of the proposed method has been evaluated on 13 benchmark classification problems and compared with five state-of-the-art feature selection methods