Abstract
Attribute reduction is a basic issue in knowledge representation and data mining. It simplifies an information system by discarding some redundant attributes. In this paper, we present a hybrid approach that combines the nature of variable neighbourhood search in the first phase with an iterated local search in the second phase that always accepts best solutions. The approach is tested over 13 well-known established datasets. The results demonstrate that the variable neighbourhood search approach is able to produce solutions that are competitive with those state-of-the-art techniques from the literature in terms of minimal reducts.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 |
| Pages | 1015-1020 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2010 |
| Event | 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 - Cairo Duration: 29 Nov 2010 → 1 Dec 2010 |
Other
| Other | 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 |
|---|---|
| City | Cairo |
| Period | 29/11/10 → 1/12/10 |
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Keywords
- Attribute reduction
- Iterated local search
- Variable neighbourhood search
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Hardware and Architecture
Cite this
Hybrid variable neighbourhood search algorithm for attribute reduction in rough set theory. / Arajy, Yahya Z.; Abdullah, Salwani.
Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10. 2010. p. 1015-1020 5687053.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Hybrid variable neighbourhood search algorithm for attribute reduction in rough set theory
AU - Arajy, Yahya Z.
AU - Abdullah, Salwani
PY - 2010
Y1 - 2010
N2 - Attribute reduction is a basic issue in knowledge representation and data mining. It simplifies an information system by discarding some redundant attributes. In this paper, we present a hybrid approach that combines the nature of variable neighbourhood search in the first phase with an iterated local search in the second phase that always accepts best solutions. The approach is tested over 13 well-known established datasets. The results demonstrate that the variable neighbourhood search approach is able to produce solutions that are competitive with those state-of-the-art techniques from the literature in terms of minimal reducts.
AB - Attribute reduction is a basic issue in knowledge representation and data mining. It simplifies an information system by discarding some redundant attributes. In this paper, we present a hybrid approach that combines the nature of variable neighbourhood search in the first phase with an iterated local search in the second phase that always accepts best solutions. The approach is tested over 13 well-known established datasets. The results demonstrate that the variable neighbourhood search approach is able to produce solutions that are competitive with those state-of-the-art techniques from the literature in terms of minimal reducts.
KW - Attribute reduction
KW - Iterated local search
KW - Variable neighbourhood search
UR - http://www.scopus.com/inward/record.url?scp=79851469991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79851469991&partnerID=8YFLogxK
U2 - 10.1109/ISDA.2010.5687053
DO - 10.1109/ISDA.2010.5687053
M3 - Conference contribution
AN - SCOPUS:79851469991
SN - 9781424481354
SP - 1015
EP - 1020
BT - Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
ER -