A constructive hyper-heuristics for rough set attribute reduction

Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

Volumn , Issue , 2010, Pages 1032-1035

  Abdullah, Salwani ^a   Sabar, Nasser R ^a   Nazri, Mohd Zakree Ahmad ^a   Turabieh, Hamza ^a   McCollum, Barry ^b  

^a UNIVERSITI KEBANGSAAN MALAYSIA   (Malaysia)

^b QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

Attribute reduction; Hyper heuristics; Rough set theory

Indexed keywords

ATTRIBUTE REDUCTION; DATA SETS; HYPER-HEURISTICS; HYPERHEURISTIC; LOW LEVEL; METAHEURISTIC; ROUGH SET; ROULETTE WHEEL SELECTION; SEARCH METHOD;

BENCHMARKING; INTELLIGENT SYSTEMS; SYSTEMS ANALYSIS;

ROUGH SET THEORY;

EID: 79851475798     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISDA.2010.5687052     Document Type: Conference Paper

References (14)

1. Z. Pawlak. Rough sets. Internat. J. Comput. Inform. Sci. 11 (5), 341-356 (1982).
2. Z. Pawlak. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, Boston (1991).
3. R. Jensen, Q. Shen. Finding Rough set reducts with ant colony optimization. In: Proc. of 2003 UK Workshop Computational Intelligence, pp. 15-22 (2003).
4. Ke Liangjun, Zuren Feng, Ren. Zhigang. An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recognition Letters 29 1351-1357 (2008).
5. J. Wang, A. Hedar, G. Zheng, S. Wang. Scatter Search for Rough Set Attribute Reduction. In proceeding of International Joint Conference on Volume 1, 24-26 April 2009 Page(s):531 - 535. (2009).
6. A. Hedar, J. Wang, M. Fukushima. Tabu search for attribute reduction in rough set theory. Technical Report 2006-08, Department of Applied Mathematics and Physics, Kyoto University (2006).
7. C.L. Blake and C.J. Merz. UCI Repository of machine learning databases. Irvine, University of California. 1998. http://www.ics.uci.edu/~mlearn/.
8. R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering, Vol. 16, No. 12, pp. 1457-1471 (2004). (Pubitemid 40010921)
9. Duntsch, G. Gediga. Uncertainty measures of rough set prediction", Artificial Intelligence, 1998, 106, pp.109-137 (1998). (Pubitemid 128402352)
10. Z. WenXiu, Q. GuoFang, W. WeiZhi. A general approach to attribute reduction in rough set theory. Science in China Series F: Information Sciences. 188-197. (2007).
11. L.A. Zadeh. Fuzzy sets. Information and Control, Vol. 8, pp. 338-353. (1965).
12. A. Skowron and J.W. Grzymala-Busse. From rough set theory to evidence theory. In Advances in the Dempster-Shafer Theory of Evidence, (R. Yager, M. Fedrizzi, and J. Kasprzyk eds.), John Wiley & Sons, Inc. (1994).
13. Burke E.K., Hyde M., Kendall G., Ochoa G., Ozcan E., Woodward J. A classification of hyper-heuristic approaches. Tech. Rep. Computer science technical report No. NOTTCS-TR-SUB-0907061259-5808, School of computer science and information technology, University of Nottingham (2009).
14. Baker, J. (1987). Reducing Bias and Inefficiency in the Selection Algorithm. In Grefenstette, J. (Ed.), Proceedings of the Second International Conference on Genetic Algorithms (pp. 14(21). Hillsdale, NJ: Lawrence Erlbaum Associates.