Abstract
This paper proposes a new feature selection method based on rough sets to take away the unnecessary attributes for the classification process from partially uncertain decision system. The uncertainty exists only in the decision attributes (classes) and is represented by the belief function theory. The simplification of the uncertain decision table to generate more significant attributes is based on computing all possible reducts. To obtain these reducts, we propose a new definition of the concepts of discernibility matrix and function under the belief function framework. Experimentations have been done to evaluate this exhaustive solution.
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References
Bauer, M.: Approximations algorithm and decision making in the Dempster-Shafer theory of evidence - an empirical study. International Journal of Approximate Reasoning 17(2-3), 217–237 (1997)
Bosse, E., Jousseleme, A.L., Grenier, D.: A new distance between two bodies of evidence. Information Fusion 2, 91–101 (2001)
Deng, D., Huang, H.: A new discernibility matrix and function. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 114–121. Springer, Heidelberg (2006)
Elouedi, Z., Mellouli, K., Smets, P.: Assessing sensor reliability for multisensor data fusion within the transferable belief model. IEEE Trans. Syst. Man Cybern. 34(1), 782–787 (2004)
Fixen, D., Mahler, R.P.S.: The modified Dempster-Shafer approach to classification. IEEE Trans. Syst. Man Cybern. 27(1), 96–104 (1997)
Hu, X., Cercone, N.: Learning in Relational Databases: a Rough Set Approach. Computational Intelligence 2, 323–337 (1995)
Modrzejewski, M.: Feature selection using rough sets theory. In: Proceedings of the 11th International Conference on Machine Learning, pp. 213–226 (1993)
Lingras, P., West, C.: Interval Set Clustering of Web Users with Rough K-means. Journal of Intelligent Information Systems 23(1), 5–16 (2004)
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)
Pawlak, Z., Zdzislaw, A.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991) ISBN 0-7923-1472-7
Pawlak, Z., Rauszer, C.M.: Dependency of attributes in Information systems. Bull. Polish Acad. Sci., Math. 33, 551–559 (1985)
Rauszer, C.M.: Reducts in Information systems. Fundamenta Informaticae (1990)
Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Infor- mation Systems. In: Slowiski, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Set Theory, pp. 311–362. Kluwer Academic Publishers, Dordrecht (1992)
Skowron, A.: Rough Sets in KDD. Special Invited Speaking, WCC 2000 in Beijing (August 2000)
Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66, 191–236 (1994)
Smets, P.: The transferable belief model for quantified belief representation. In: Gabbay, D.M., Smets, P. (eds.) Handbook of Defeasible Reasoning and Uncertainty Management Systems, vol. 1, pp. 207–301. Kluwer, Doordrecht (1998)
Trabelsi, S., Elouedi, Z.: Heuristic method for attribute selection from partially uncertain data using rough sets. International Journal of General Systems 39(3), 271–290 (2010)
Trabelsi, S., Elouedi, Z., Lingras, P.: Heuristic for Attribute Selection Using Belief Discernibility Matrix. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J., Janicki, R., Hassanien, A.E., Yu, H. (eds.) RSKT 2012. LNCS (LNAI), vol. 7414, pp. 129–138. Springer, Heidelberg (2012)
Trabelsi, S., Elouedi, Z., Lingras, P.: Belief Rough Set Classifier. In: Gao, Y., Japkowicz, N. (eds.) AI 2009. LNCS (LNAI), vol. 5549, pp. 257–261. Springer, Heidelberg (2009)
Yao, Y., Zhao, Y.: Discernibility Matrix Simplification for Constructing Attribute Reducts. Information Sciences 179(5), 867–882 (2009)
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Trabelsi, S., Elouedi, Z., Lingras, P. (2013). Exhaustive Search with Belief Discernibility Matrix and Function. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_14
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DOI: https://doi.org/10.1007/978-3-642-38457-8_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38456-1
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