Tabu search for attribute reduction in rough set theory |
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| Authors: | Abdel-Rahman Hedar Jue Wang Masao Fukushima |
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| Affiliation: | (1) Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan;(2) Department of Computer Science, Faculty of Computer and Information Sciences, Assiut University, Assiut, Egypt;(3) Academy of Mathematics and System Science, Chinese Academy of Science, Beijing, 710071, People’s Republic of China |
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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. |
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| Keywords: | Computational intelligence Granular computing Attribute reduction Rough set Tabu search |
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