Abstract
In this paper, we deal with the problem of rule discovery process based on rough sets from partially uncertain data. The uncertainty exists only in decision attribute values and is handled by the Transferable Belief Model (TBM), one interpretation of the belief function theory. To solve this problem, we propose in this uncertain environment, a new method based on a soft hybrid induction system for discovering classification rules called GDT-RS which is a hybridization of the Generalization Distribution Table and the Rough Set methodology.
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References
Bauer, M.: Approximations algorithm and decision making in the Dempster-Shafer theory of evidence - an empirical study. IJAR 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)
Denoeux, T., Skarstien-Bajanger, M.: Induction of decision trees form partially classified data using belief functions. In: Proceedings of the IEEE International Conference on Systems, Nashiville, USA, vol. 4, pp. 2923–2928 (2000)
Dong, J.Z., Zhong, N., Ohsuga, S.: Probabilistic rough induction: The GDS-RS methodology and algorithms. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol. 1609, pp. 621–629. Springer, Heidelberg (1999)
Elouedi, Z., Mellouli, K., Smets, P.: Belief decision trees: Theoretical foundations. International Journal of Approximate Reasoning, Vol 28(2-3), 91–124 (2001)
Elouedi, Z., Mellouli, K., Smets, P.: Assessing sensor reliability for multisensor data fusion within the transferable belief model. IEEE Trans. Syst. Man cyben. 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)
Grzymala-Busse, J.W.: Rough set strategies to data with missing attribute values. In: Workshop Notes, Foundations and New Directions of Data Mining, the 3rd International Conference on Data Mining, Melbourne, Florida, 56–63 (2003)
Grzymala-Busse, J.W., Siddhaye, S.: Rough Set Approaches to Rule Induction from Incomplete Data. In: Proceedings of the IPMU 2004, Perugia, Italy, July 4-9, vol. 2, pp. 923–930 (2004)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)
Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support, pp. 331–362. Kluwer Academic Publishers, Boston (1992)
Skowron, A., Grzymala-Busse, J.W.: From rough set theory to evidence theory. In: Advances in the Dempster-Shafer Theory of Evidence, New York, pp. 193–236 (1994)
Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66(2), 191–234 (1994)
Tessem, B.: Approximations for efficient computation in the theory of evidence. Artif. Intell. 61(2), 315–329 (1993)
Trabelsi, S., Elouedi, Z., Mellouli, K.: Pruning belief decision tree methods in averaging and conjunctive approaches. International Journal of Approximate Reasoning 46, 91–124 (2007)
Trabelsi, S., Elouedi, Z., Lingras, P.: Dynamic Reduct from Partially Uncertain Data Using Rough Sets. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol. 5908, pp. 160–167. Springer, Heidelberg (2009)
Vannoorenberghe, P.: On aggregating belief decision trees. Information Fusion 5(2), 179–188 (2004)
Zhong, N., Dong, J.Z., Ohsuga, S.: Data mining: A probabilistic rough set approach. Rough Sets in Knowledge Discovery 2, 127–146 (1998)
Zhong, N., Dong, J.Z., Ohsuga, S.: Using generalization distribution tables as a hypotheses search space for generalization. In: Proceedings of fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery (RSFD-96), pp. 396–403 (1996)
Zouhal, L.M., Denoeux, T.: An evidence-theory k-NN rule with parameter optimization. IEEE Trans. Syst. Man Cybern. C 28(2), 263–271 (1998)
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Trabelsi, S., Elouedi, Z., Lingras, P. (2010). Rule Discovery Process Based on Rough Sets under the Belief Function Framework. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_74
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DOI: https://doi.org/10.1007/978-3-642-14049-5_74
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