Rough Set Approximate Entropy Reducts With Order Based Particle Swarm Optimization

被引：0
|
作者：
Wang, Xiangyang [1 ]
Wan, Wanggen [1 ]
Yu, Xiaoqing [1 ]
机构：
[1] Shanghai Univ, Sch Commun & Informat Engn, Shanghai 200072, Peoples R China
来源：
WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09) | 2009年
关键词：
Rough Set; Approximate Entropy Reducts; Particle Swarm Optimization; Order-based Particle Swarm Optimization; Hybrid Algorithm; FEATURE-SELECTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We propose an order-based Particle Swarm Optimization (o-PSO) hybrid algorithm for rough set approximate entropy reducts (oPSOAER). The o-PSO generates proper permutation of attributes, which are used by approximate entropy reduction algorithm to produce rough set reducts. The reducts are evaluated by fitness function. The primary criterion of optimization of the fitness function is the number of rules and the secondary is the reduct length. Our algorithm is tested on some UCI datasets. The results show that oSPOAER is efficient for approximate entropy reducts. The approximate entropy reducts optimized according to number of rules are better in classification algorithms than the shortest ones, and are much better for practical applications.
引用
收藏
页码：553 / 559
页数：7
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