Heuristic method for attribute selection from partially uncertain data using rough sets

International Journal of General Systems

Volumn 39, Issue 3, 2010, Pages 271-290

  Trabelsi, Salsabil ^a   Elouedi, Zied ^a  

^a Higher Institute of Management of Tunis   (Tunisia)

Author keywords

Attribute selection; Belief function theory; Classification; Heuristic; Rough sets; Uncertainty

Indexed keywords

ATTRIBUTE SELECTION; BELIEF FUNCTION THEORY; CLASSIFICATION PROCESS; DECISION ATTRIBUTE; DECISION RULES; EVALUATION CRITERIA; ROUGH SET; TIME COMPLEXITY; TRANSFERABLE BELIEF MODEL; UNCERTAIN DATAS;

DECISION TABLES; DECISION THEORY; FEATURE EXTRACTION; ROUGH SET THEORY; UNCERTAINTY ANALYSIS;

HEURISTIC METHODS;

EID: 77949291930     PISSN: 03081079     EISSN: 15635104     Source Type: Journal    
DOI: 10.1080/03081071003625305     Document Type: Conference Paper

References (22)

1. Ben Hariz, S., Elouedi, Z., and Mellouli, K., 2006. Clustering approach using belief function theory. In: 12th international conference. AIMSA 2006, Varna, Bulgaria, 12-15 September, LNAI 4183 Berlin/Heidelberg: Springer, 162-171.
2. Chouchoulas, A. and Shen, Q., 2001. Rough set-aided keyword reduction for text categorisation. Journal of applied artificial intelligence, 15 (9), 843-873.
3. Denoeux, T., 1995. A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE transactions on systems, man and cybernetics, 25 (5), 804-813.
4. Denoeux, T., 2000. A neural network classifier based on Dempster-Shafer theory. IEEE transactions on systems, man and cybernetics, part A, 30 (2), 131-150.
5. Elouedi, Z., Mellouli, K., and Smets, P., 2001. Belief decision trees: theoretical foundations. International journal of approximate journal, 28 (2-3), 91-124.
6. Grzymala-Busse, J.W., 2003. 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, FL, 5663.
7. Jensen, R. and Shen, Q., 2003. Finding rough set reducts with ant colony optimization. In: Proceedings of the 2003 UK workshop on computational intelligence, 15-22.
8. Kryszkiewiez, M., 1995. Rough set approach to incomplete information systems. In: Proceedings of the second annual joint conference on information science, Wrightsville, NC, 194-197.
9. Modrzejewski, M., 1993. Feature selection using rough sets theory. In: Proceedings of the 11th international conference on Machine Learning, Madison, WI, pp. 213-226.
10. Orlowska, E., 1994. Reasoning with incomplete information: rough set based information systems. In: Incompleteness and uncertainty in information systems. New York, NY: Springer-Verlag, 16-33.
11. Pawlak, Z., 1982. Rough sets. International journal of computer and information sciences, 11 (5), 341-356.
12. Pawlak, Z., 1991. Rough sets: theoretical aspects of reasoning about data. Dordrecht: Kluwer Academic Publishing.
13. Shafer, G., 1976. A mathematical theory of evidence. Princeton, NJ: Princeton University Press.
14. Skowron, A. and Rauszer, C., 1992. The discernibility matrices and functions in information systems. In: R. Slowinski, ed. Intelligent decision support. Boston, MA: Kluwer Academic Publishers, 331-362.
15. Slowinski, R. and Stefanowski, J., 1989. Rough classification in incomplete information systems. Mathematical and computer modelling, 12 (10-11), 1347-1357.
16. Smets, P., 1994. The transferable belief model. Artificial intelligence, 66 (2), 191-236.
17. Smets, P., 1998a. The transferable belief model for quantified belief representation. In: D.M. Gabbay and P. Smets, eds. Handbook of defeasible reasoning and uncertainty management systems. Vol 1. Doordrecht: Kluwer, 207-301.
18. Smets, P., 1998b. Application of belief transferable belief model to diagnostic problems. International journal of intelligent systems, 13 (2-3), 127-157.
19. Trabelsi, S. and Elouedi, Z., 2008. Learning decision rules from uncertain data using rough sets. In: The 8th international FLINS conference on computational intelligence in decision and control. Madrid, Spain, 21-24 September, Singapore: World Scientific, 114-119.
20. Trabelsi, S. and Elouedi, Z., 2009. Attribute selection from partially uncertain data using rough sets. The third international conference on modeling simulation, and applied optimization, UAE, January 20-22.
21. Wroblewski, J., 1995. Finding minimal reducts using genetic algorithms. In: Proceedings of the 2nd annual joint conference on information sciences. Wrightsville Beach, NC 186-189.
22. Zhong, N., Dong, J.Z., and Ohsuga, S., 2001. Using rough sets with heuristics for feature selection. Journal of intelligent information systems, 16 (3), 199-214.