Feature selection based on rough sets and particle swarm optimization

Pattern Recognition Letters - Tập 28 Số 4 - Trang 459-471 - 2007
Xiangyang Wang1, Jie Yang1, Xiaolong Teng1, Weijun Xia2, Richard Jensen3
1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200030, China
2Institute of Automation,Shanghai Jiao Tong University,Shanghai 200030,China)
3Department of Computer Science, The University of Wales, Aberystwyth, Ceredigion, SY23 3DB Wales, UK

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Tài liệu tham khảo

Bazan, 1998, A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision table, 321

Bazan, 2000, Rough set algorithms in classification problem, 49

Bell, 1998, Computational methods for rough classification and discovery, J. ASIS, 49, 403

Bjorvand, A.T., 1997. ‘Rough Enough’—a system supporting the rough sets approach. In: Sixth Scandinavian Conference on Artificial Intelligence SCAI’97.

Bjorvand, 1997, Vol. 4

Blake, C., Keogh, E., Merz, C.J., 1998. UCI repository of machine learning databases. Technical Report, Department of Information and Computer Science, University of California, Irvine, CA. <http://www.ics.uci.edu/mlearn/MLRepository.htm>.

Chouchoulas, 2001, Rough set-aided keyword reduction for text categorization, Appl. Artif. Intell., 15, 843, 10.1080/088395101753210773

Eberhart R.C., Shi, Y., 2001. Particle swarm optimization: developments, applications and resources. In: Proceedings of IEEE International Conference on Evolutionary Computation. Seoul, pp. 81–86.

Guyon, 2003, An introduction to variable and feature selection, J. Mach. Learning Res., 3, 1157

Hu, X., 1995. Knowledge discovery in databases: an attribute-oriented rough set approach, Ph.D. thesis, Regina University.

Hu, 1995, Learning in relational databases: a rough set approach, Comput. Intell., 11, 323, 10.1111/j.1467-8640.1995.tb00035.x

Hu, 2003, Feature ranking in rough sets, AI Commun., 16, 41

Janusz, 2000, A mathematical foundation for improved reduct generation in information systems, Knowledge Informat. Syst., 2, 131, 10.1007/s101150050007

Jensen, R., Shen, Q., 2003. Finding rough set reducts with ant colony optimization. In: Proceedings of the 2003 UK Workshop on Computational Intelligence, pp. 15–22.

Kennedy, J., 1997. The particle swarm: social adaptation of knowledge. In: IEEE International Conference on Evolutionary Computation, April 13–16, pp. 303–308.

Kennedy, J., Eberhart, R.C., 1995a. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, pp. 1942–1948.

Kennedy, J., Eberhart, R.C., 1995b. A new optimizer using particle swarm theory. In: Sixth International Symposium on Micro Machine and Human Science. Nagoya, pp. 39–43.

Kennedy, J., Spears, W.M., 1998. Matching algorithms to problems: an experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In: Proceedings of the IEEE International Conference on Evolutionary Computation. pp. 39–43.

Komorowski, 1999, Rough sets: a tutorial, 3

Kudo, 2000, Comparison of algorithms that select features for pattern classifiers, Pattern Recognition, 33, 25, 10.1016/S0031-3203(99)00041-2

Liu, 1998

Nguyen, H.S., 1996. Some efficient algorithms for rough set methods. In: Proceedings of the Sixth International Conference, Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU’96) 2, July 1–5, 1996, Granada, Spain pp. 1451–1456.

2003, Pattern Recognition Lett., 24, 829, 10.1016/S0167-8655(02)00195-2

Pawlak, 1982, Rough Sets, Int. J. Comput. Informat. Sci., 11, 341, 10.1007/BF01001956

Pawlak, 1991

Pawlak, 1997, Rough set approach to knowledge-based decision support, Eur. J. Operat. Res., 99, 48, 10.1016/S0377-2217(96)00382-7

Shi, Y., Eberhart, R.C., 1998a. A modified particle swarm optimizer. In: Proc. IEEE Int. Conf. on Evolutionary Computation. Anchorage, AK, USA, pp. 69–73.

Shi, 1998, Parameter selection in particle swarm optimization, 591

Shi, 1999, Empirical study of particle swarm optimization, 1945

Skowron, 1992, The discernibility matrices and functions in information systems, 311

Skowron, A., Bazan, J., Son, N.H., Wroblewski, J., et al., 2005a. RSES 2.2 User’s Guide. Institute of Mathematics, Warsaw University, Warsaw, Poland, January 19, 2005. <http://logic.mimuw.edu.pl/~rses>.

Skowron, A., Wang, H., Wojna, A., Bazan J., 2005b. A hierarchical approach to multimodal classification. In: Slezak, D., Wang, G., Szczuka, M., Duentsch, I., Yao, Y.Y., (Eds.), Rough sets, fuzzy sets, data mining, and granular computing. In: Proc. 10th Int. Conf. RSFDGrC 2005, Regina, Canada, September 1–3, 2005, Part 2, Lecture Notes in Artificial Intelligence 3642, Springer, Heidelberg, 2005, pp. 119–127.

Slezak, D., 1996. Approximate reducts in decision tables. In: Proc. of IPMU’96, 1996.

Slezak, D., Wroblewski, J., 2003. Order based genetic algorithms for the search of approximate entropy reducts. In: Wang, G.Y., et al. (Eds.), RSFDGrC. LNAI, Vol. 2639. Chongqing, China, 2003, pp. 308–311.

Starzyk, 1998, Reduct generation in information systems, Bull. Int. Rough Set Society, 3, 19

Stefanowski, 1998, On rough set based approaches to induction of decision rules, Vol. 1, 500

Susmaga, R., 1998. Parallel computation of reducts. In: Polkowski, L., Skowron, A. (Eds.), RSCTC’98, LNAI 1424, 1998, pp. 450–458.

Susmaga, 1996, Reducts and constructs in attribute reduction, 61(2)

Susmaga, R., 2004b. Tree-Like Parallelization of Reduct and Construct Computation. In: Tsumoto, S., et al. (Eds.), RSCTC 2004, LNAI 3066, 2004, pp. 455–464.

Swiniarski, 2003, Rough set methods in feature selection and recognition, Pattern Recognition Lett., 24, 833, 10.1016/S0167-8655(02)00196-4

Vafaie, H., Imam, I.F., 1994. Feature selection methods: genetic algorithms vs. greedy-like search. In: Proc. Int. Conf. on Fuzzy and Intelligent Control Systems.

Wang, 2002, Decision table reduction based on conditional information entropy, Chin. J. Comput., 25, 759

Wang, G.Y., Zhao, J., 2004. Theoretical study on attribute reduction of rough set theory: comparison of algebra and information views. In: Proc. Third IEEE Int. Conf. on Cognitive Informatics.

Wroblewski, J., 1995. Finding minimal reducts using genetic algorithms. In: Proc. Second Annual Join Conf. on Information Sciences, Wrightsville Beach, NC. September 28–October 1, pp. 186–189.

Wroblewski, 1996, Theoretical foundations of order-based genetic algorithms, 28(3–4)

Zhai, 2002, Feature extraction using rough set theory and genetic algorithms—an application for the simplification of product quality evaluation, Comput. Industrial Eng., 43, 661, 10.1016/S0360-8352(02)00131-6