Scatter Search for Rough Set Attribute Reduction

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.

作者: Jue Wang Abdel-Rahman Hedar Guihuan Zheng Shouyang Wang

作者单位: Academy of Math, and Systems Science Chinese Academy of Sciences Beijing 100190, P.R.China Dept.of Applied Math.& Physics Kyoto University Kyoto, Japan

会议类型: 国际会议

会议名称: The Second International Joint Conference on Computational Science and Optimization(CSO 2009)(2009 国际计算科学与优化会议)

会议地点: 三亚

会议语种:英文

页码: 531-535

在线出版日期: 2009-04-24（万方平台首次上网日期，不代表论文的发表时间）