Improvement of Discrete Particle Swarm Classification System
The Discrete Particle Swarm Optimization (DPSO) has little parameters and high convergent capability of the global optimizing. Based on the existing PSObased classification system we constructed a new classification system based on Discrete PSO. We used the variable-length method to represent particle in the process of operation, represent rule set in a reasonable way and do some appropriate cut, and use the default rule to improve classification effectiveness. The experimental results shown that the system can be cut the rules accurately. It could work well with less number of the rules and desired classification accuracy; the classification system has good performance.
Particle Swarm Optimization Fuzzy Clustering Discrete PSO Rule set
Hao Wang Yan Zhang
School of Computer and Information Fuyang Teachers College Fuyang, China
国际会议
上海
英文
1067-1071
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）