A rough set approach to feature selection based on ant colony optimization

Bonabeau, 1999

Dash, 1997, Feature selection for classification, Intell. Data Anal., 1, 131, 10.1016/S1088-467X(97)00008-5

Dash, 2003, Consistency-based search in feature selection, Artif. Intell., 151, 155, 10.1016/S0004-3702(03)00079-1

Deogun, 1998, Feature selection and effective classifiers, J. ASIS, 5, 403

Dorigo, M., Caro, G.D., 1999. Ant colony optimization: A new meta-heuristic. In: Proc. Congress on Evolutionary Computing.

Dorigo, 1996, The Ant system: Optimization by a colony of cooperating agents, IEEE Trans. Syst. Man Cybernet. Part B, 26, 29, 10.1109/3477.484436

Duan, 2007, Personalized Web retrieval based on rough-fuzzy method, J. Comput. Inform. Systems, 3, 1067

Gasca, 2006, Eliminating redundancy and irrelevance using a new MLP-based feature selection method, Pattern Recognition, 39, 313, 10.1016/j.patcog.2005.09.002

Guyon, 2002, Gene selection for cancer classification using support vector machines, Machine Learn., 46, 389, 10.1023/A:1012487302797

Hedar, A., Wang, J., Fukushima, M., 2006. Tabu search for attribute reduction in rough set theory, Technical Report 2006-008, Department of Applied Mathematics and Physics, Kyoto University.

Hu, X., 1995. Knowledge discovery in databases: An attribute oriented rough set approach. Ph.D. Thesis, Regina University.

Jensen, R., 2005. Combining rough and fuzzy sets for feature selection, Ph.D. Thesis, Univ. Of Edinburgh.

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

Jensen, 2004, Semantics-preserve dimensionality reduction: Rough and fuzzy-rough-based approaches, IEEE Trans. Knowledge Data Eng., 16, 1457, 10.1109/TKDE.2004.96

Kira, K., Rendell, L.A., 1992. The feature selection problem: Traditional methods and a new algorithm. In: Proc. AAAI-92, San Jose, CA, pp. 129–134.

Kohavi, R., 1994. Feature subset selection using the wrapper method: Overfitting and dynamic search space topology. In: Proc. AAAI Fall Symposium on Relevance, pp. 109–113.

Lai, 2006, Random subspace method for multivariate feature selection, Pattern Recognition Lett., 27, 1067, 10.1016/j.patrec.2005.12.018

Liu, 1998

Maniezzo, 1999, The ant system applied to the quadratic assignment problem, Knowledge Data Eng., 11, 769, 10.1109/69.806935

Miao, 2004, A comparison of rough set methods and representative inductive learning algorithms, Fundamenta Inform., 59, 203

Miao, D.Q., Wang, J., 1997. Information-based algorithm for reduction of knowledge. IEEE Internat. Conf. Intell. Process. Systems, Beijing, China, 1155–1158.

Mi, 2004, Approaches to knowledge reduction based on variable precision rough set model, Inform. Sci., 159, 255, 10.1016/j.ins.2003.07.004

Modrzejewski, M., 1993. Feature selection using rough sets theory. In: Proceedings of the European Conference on Machine Learning, Vienna, Austria, pp. 213–226.

Neumann, 2005, Combined SVM-based feature selection and classification, Machine Learn., 61, 129, 10.1007/s10994-005-1505-9

Pal, 1999

Pawlak, 1982, Rough sets, Internat. J. Comput. Inform. Sci., 11, 341, 10.1007/BF01001956

Raymer, 2000, Dimensionality reduction using genetic algorithms, IEEE Trans. Evol. Comput., 4, 164, 10.1109/4235.850656

Shannon, 1949

Somol, 2004, Fast branch & bound algorithms for optimal feature selection, IEEE Trans. Pattern Anal. Machine Intell., 26, 900, 10.1109/TPAMI.2004.28

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

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

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

Xie, Z.X., Hu, Q.H., Yu, D.R., 2006. Improved feature selection algorithm based on SVM and correlation, ISNN1, pp. 1373–1380.

Yao, 2003, Information-theoretic measures for knowledge discovery and data mining, 115

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

Zhong, 2001, Using rough sets with heuristics for feature selection, J. Intell. Inform. Systems, 16, 199, 10.1023/A:1011219601502