点击次数：

论文类型：会议论文

发表时间：2007-06-18

收录刊物：EI、CPCI-S

卷号：4527

期号：PART 1

页面范围：397-+

摘要：Finding appropriate features is one of the key problems in the increasing applications of rough set theory, which is also one of the bottlenecks of the rough set methodology. Particle Swarm Optimization (PSO) is particularly attractive for this challenging problem. In this paper, we attempt to solve the problem using a particle swarm optimization approach. The proposed approach discover the best feature combinations in an efficient way to observe the change of positive region as the particles proceed through the search space. We evaluate the performance of the proposed PSO algorithm with Genetic Algorithm (GA). Empirical results indicate that the proposed algorithm could be an ideal approach for solving the feature reduction problem when other algorithms failed to give a better solution.