An Improved Niching Binary Particle Swarm Optimization For Feature Selection

With the rapid growth of information, feature selection has become an important step before data classification. Evolutionary algorithms can be used to solve feature selection given their effective search capabilities since the traditional feature selection cann't consider the combination effect. In this paper, we present a novel filter feature selection using a niching binary particle swarm optimization. The entire population is divided into several niche groups to maintain the diversity of evolutionary environment. A new framework based on three different kinds of topologies is proposed which can avoid falling into the local optimum and enhance global searching capabilities. In this framework, when the optimal fitness value has stagnated for 20 generations, the connection between particles in each niche group and the connection between niche center particles will change to improve the optimal fitness value. The above procedure can produce a subset of features. In order to verify the effectiveness of the proposed algorithm, we have tested on five data sets in the UCI database. The experimental results show that the proposed algorithm can effectively search for the feature space and verify the efficiency of the obtained feature subsets under different classifiers.