关键词: 二进制粒子群优化算法, 特征聚类, 交互操作, 粒子密度, 群智能算法

Abstract: Feature selection is an important step of data preprocessing in data mining, so selecting the optimal feature subset can reduce the data dimension and computing cost of learning algorithm effectively. In this paper,binary particle swarm optimization (BPSO) is used to optimize the feature selection process. A strategy of population initialization based on feature clustering information is proposed, in which feature clustering is completed by community partitioning algorithm. According to the partitioned information, information redundancy is reduced and the quality of initial population is improved. Then, an adaptive local search strategy based on decision space similarity is proposed, in which the similarity index of particles is determined by the similarity of particles in decision space. In the evolutionary process, the particles are adaptively adjusted for local search to avoid premature algorithm. Finally, 3 representative optimization algorithms are selected to carry out comparative experiments on 11 UCI datasets. The experimental results show that the improved BPSO algorithm has a better performance in reducing the number of features than other comparison algorithms, and the classification accuracy is also significantly improved.

Key words: binary particle swarm optimization, feature clustering, interactive manipulation, particle density, swarm intelligence algorithms