Rule discovery process based on rough sets under the belief function framework

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6178 LNAI, Issue , 2010, Pages 726-736

  Trabelsi, Salsabil ^a   Elouedi, Zied ^a   Lingras, Pawan ^b  

^a UNIVERSITÉ DE TUNIS   (Tunisia)

^b SAINT MARY S UNIVERSITY   (Canada)

Author keywords

belief function theory; classification; generalization distribution table; Rough sets; uncertainty

Indexed keywords

BELIEF FUNCTION; BELIEF FUNCTION THEORY; CLASSIFICATION RULES; DECISION ATTRIBUTE; INDUCTION SYSTEM; ROUGH SET; RULE DISCOVERY; TRANSFERABLE BELIEF MODEL; UNCERTAIN DATAS; UNCERTAIN ENVIRONMENTS;

ARTIFICIAL INTELLIGENCE; DATA PROCESSING; DECISION THEORY; KNOWLEDGE BASED SYSTEMS; UNCERTAINTY ANALYSIS;

ROUGH SET THEORY;

EID: 77954878266     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-14049-5_74     Document Type: Conference Paper

References (21)

1. Bauer, M.: Approximations algorithm and decision making in the Dempster-Shafer theory of evidence - an empirical study. IJAR 17(2-3), 217-237 (1997)
2. Bosse, E., Jousseleme, A.L., Grenier, D.: A new distance between two bodies of evidence. Information Fusion 2, 91-101 (2001)
3. 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)
4. Dong, J.Z., Zhong, N., Ohsuga, S.: Probabilistic rough induction: The GDS-RS methodology and algorithms. In: Rás, Z.W., Skowron, A. (eds.) ISMIS 1999. LNCS, vol.1609, pp. 621-629. Springer, Heidelberg (1999)
5. Elouedi, Z., Mellouli, K., Smets, P.: Belief decision trees: Theoretical foundations. International Journal of Approximate Reasoning, Vol 28(2-3), 91-124 (2001)
6. 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)
7. Fixen, D., Mahler, R.P.S.: The modified Dempster-Shafer approach to classification. IEEE Trans. Syst. Man Cybern. 27(1), 96-104 (1997)
8. 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)
9. 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)
10. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)
11. Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)
12. 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)
13. 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)
14. Smets, P., Kennes, R.: The transferable belief model. Artificial Intelligence 66(2), 191-234 (1994)
15. Tessem, B.: Approximations for efficient computation in the theory of evidence. Artif. Intell. 61(2), 315-329 (1993)
16. 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)
17. Trabelsi, S., Elouedi, Z., Lingras, P.: Dynamic Reduct from Partially Uncertain Data Using Rough Sets. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., ́Sl-ezak, D., Zhu,W. (eds.) RSFDGrC 2009. LNCS (LNAI), vol.5908, pp. 160-167. Springer, Heidelberg (2009)
18. Vannoorenberghe, P.: On aggregating belief decision trees. Information Fusion 5(2), 179-188 (2004)
19. Zhong, N., Dong, J.Z., Ohsuga, S.: Data mining: A probabilistic rough set approach. Rough Sets in Knowledge Discovery 2, 127-146 (1998)
20. 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)
21. 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)