Scatter Search for Rough Set Attribute Reduction

被引：5
|
作者：
Wang, Jue [1 ]
Hedar, Abdel-Rahman [2 ]
Wang, Shouyang [1 ]
机构：
[1] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
[2] Kyoto Univ, Dept Appl Math & Phys, Kyoto, Japan
来源：
2007 SECOND INTERNATIONAL CONFERENCE ON BIO-INSPIRED COMPUTING: THEORIES AND APPLICATIONS | 2007年
基金：
中国国家自然科学基金;
关键词：
D O I：
10.1109/BICTA.2007.4806458
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Attribute reduction of an information system is a key problem in rough set theory and its applications. Using computational intelligence (CI) tools to solve such problems has recently fascinated many researchers. In this paper, we consider a meta-heuristic of scatter search to solve the attribute reduction problem in rough set theory. The proposed method, called scatter search attribute reduction (SSAR), shows promising and competitive performance compared with some other CI tools in terms of solution qualities. Moreover, SSAR shows a superior performance in saving the computational costs.
引用
收藏
页码：236 / +
页数：3
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