Tabu search for attribute reduction in rough set theory

Authors:
Abdel-Rahman Hedar;Jue Wang;Masao Fukushima
Affiliations:
Kyoto University, Department of Applied Mathematics and Physics, Graduate School of Informatics, 606-8501, Kyoto, Japan and Assiut University, Department of Computer Science, Faculty of Computer a ...;Kyoto University, Department of Applied Mathematics and Physics, Graduate School of Informatics, 606-8501, Kyoto, Japan and Chinese Academy of Science, Academy of Mathematics and System Science, 7 ...;Kyoto University, Department of Applied Mathematics and Physics, Graduate School of Informatics, 606-8501, Kyoto, Japan
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Year:
2008

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Abstract

In this paper, we consider a memory-based heuristic of tabu search to solve the attribute reduction problem in rough set theory. The proposed method, called tabu search attribute reduction (TSAR), is a high-level TS with long-term memory. Therefore, TSAR invokes diversification and intensification search schemes besides the TS neighborhood search methodology. TSAR shows promising and competitive performance compared with some other CI tools in terms of solution qualities. Moreover, TSAR shows a superior performance in saving the computational costs.