关键词: 多目标优化, 特征选择, 互信息（MI）, 粒子群算法（PSO）, 帕累托前沿面, 外部文档

Abstract: Feature selection is an important pre-processing in data mining, due to a lot of redundant and irrelevant features in datasets. A filter-wrapper multi-objective feature selection method based on hybrid mutual information and particle swarm optimization algorithm (HMIPSO) is proposed. Based on the number of iterations of the pbest of the particle from the last update, an adaptive mutation strategy is proposed to disturb the population and avoid the population falling into local optimum. Meanwhile, a new set concept based on the Pareto front and archive is proposed. Combining mutual information and the new set knowledge, a local search strategy is proposed, which enables particles in Pareto front to delete irrelevant and redundant features, and then the Pareto front before and after learning is updated by Elite. Finally, this paper compares the effectiveness of HMIPSO with other 4 multi-objective algorithms on 15 UCI datasets. The experimental results show that HMIPSO can reduce the number of features and classification error rate efficiently.

Key words: multi-objective optimization, feature selection, mutual information (MI), particle swarm optimization(PSO), Pareto front, archive