Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s00500-007-0260-1 | Soft Comput. |
Keywords | Field | DocType |
Computational intelligence,Granular computing,Attribute reduction,Rough set,Tabu search | Heuristic,Mathematical optimization,Computational intelligence,Computer science,Rough set,Theoretical computer science,Granular computing,Artificial intelligence,Neighborhood search,Machine learning,Tabu search | Journal |
Volume | Issue | ISSN |
12 | 9 | 1432-7643 |
Citations | PageRank | References |
50 | 2.33 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Abdel-Rahman Hedar | 1 | 404 | 30.79 |
| Jue Wang | 2 | 50 | 2.33 |
| Masao Fukushima | 3 | 197 | 14.00 |