Feature selection algorithm based on bare bones particle swarm optimization

Feature selection is a useful pre-processing technique for solving classification problems. As an almost parameter-free optimization algorithm, the bare bones particle swarm optimization (BPSO) has been applied to the topic of optimization on continuous or integer spaces, but it has not been applied to feature selection problems with binary variables. In this paper, we propose a new method to find optimal feature subset by the BPSO, called the binary BPSO. In this algorithm, a reinforced memory strategy is designed to update the local leaders of particles for avoiding the degradation of outstanding genes in the particles, and a uniform combination is proposed to balance the local exploitation and the global exploration of algorithm. Moreover, the 1-nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. Some international standard data sets are selected to evaluate the proposed algorithm. The experiments show that the proposed algorithm is competitive in terms of both classification accuracy and computational performance.