关键词: 经典粗糙集, 自适应, 粒子群优化, 离散化, 禁忌搜索

Abstract: Focusing on the issue that the classical rough set can only deal with discrete attributes, a discretization algorithm based on Adaptive Hybrid Particle Swarm Optimization (AHPSO) was proposed. Firstly, the adaptive adjustment strategy was introduced, which could not only overcome the shortage that the particle swarm was easy to fall into local extremum but also improve the ability of seeking the global excellent result. Secondly, the Tabu Search (TS) method was introduced to deal with the global optimal particle of each generation and to get the best global optimal particle, which enhanced the local search ability of particle swarm. Finally, the attribute discretization points were initialized to the particle group when the classification ability of the decision table had been kept. The optimal discretization points were sought through the interaction between particles. By using the classification method of J48 decision tree based on WEKA (Waikato Environment for Knowledge Analysis) platform, compared with the discretization algorithms based on importance of attribute and information entropy, the classification accuracy of the proposed algorithm improved by about 10% to 20%.Compared with the discretization algorithms based on Niche Discrete PSO (NDPSO) and linearly decreasing weight PSO, the classification accuracy of the proposed algorithm improved by about 2% to 5%. The experimental results show that the proposed algorithm significantly enhances the accuracy of classification by J48 decision tree, and it has better validity for discretization of continuous attributes.

Key words: classical rough set, self-adaption, Particle Swarm Optimization (PSO), discretization, Tabu Search (TS)