Scatter Search for Rough Set Attribute Reduction
Jue Wang, Abdel-Rahman Hedar, Guihuan Zheng, Shouyang Wang · 2009
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.