Non parametric fuzzy modeling of belief functions in evidence theory

Proceedings of the IASTED International Conference on Modeling and Simulation

Volumn , Issue , 2004, Pages 60-65

  Arif, M ^a   Brouard, T ^a   Vincent, N ^b  

^a UNIVERSITÉ DE TOURS   (France)

^b UNIVERSITÉ PARIS DESCARTES   (France)

Author keywords

Dempster Shafer evidence theory; Distance classifiers; Fuzzy membership function; Modeling of belief functions

Indexed keywords

DATA HANDLING; GENETIC ALGORITHMS; MATHEMATICAL MODELS; MEMBERSHIP FUNCTIONS; MONITORING; PATTERN RECOGNITION;

DEMPSTER-SHAFER EVIDENCE THEORY; DISTANCE CLASSIFIERS; FUZZY MEMBERSHIP FUNCTIONS; MODELING OF BELIEF FUNCTIONS;

FUZZY SETS;

EID: 18944369143     PISSN: 10218181     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. L Xu, A Krzyżak, C. Y Suen, Methods of Combining Multiple Classifiers and Their Applications to Handwriting Recognition, IEEE Transactions on Systems, Man and Cybernetics, 22(3) 1992, 418-435
2. C. Nadal, R. Legault, and C.Y. Suen, Complementary Algorithms for the Recognition of Totally Unconstrained Handwritten Numerals, in Proc. 10th Int. conf. Pattern Recognition., Vol. A, June 1990, 434-449.
3. G. Ng and H. Sing, Democracy in pattern classification : Combinations of Votes from Various Pattern Classifiers, Artificial Intelligence in Eng., 12 1998, 189-204.
4. G. Ng and H. Sing, Data Equalization with Evidence Combination for Pattern Recognition. Pattern Recognition Letters, 19 (1998) 227-235.
5. I. Bloch, Some Aspects of Demspter-Shafer Evidence Theory for Classification of Multi-Modality Medical Images Taking Partial Volume Effect into Account, Pattern Recognition Letters 17, 1996, 905-919.
6. A. Verikas, K. Malmqvist, and M. Bacauskiene, Combining Neural Networks, Fuzzy Sets, and Evidence Theory Based Approaches for Analyzing Colour Imges, IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como, Italy, July 2000 pp 297-302.
7. H. Kim and P. H. Swain, Evidential reasoning approach to multisource data classification in remote sensing, IEEE Transactions on Systems, Man and Cybernetics, 25(8): pp. 1257-1265, 1995.
8. E. Mandler and J. Schurman, Combining the classification results of independent classifiers based on Dempster-Shafer theory of evidence, International Journal of Pattern Recognition and Artificial intelligence, pp. 381-393, 1988.
9. W. B. Luo and B. Caselton, Using Dempster-Shafer theory to represent climate change, Journal of Environment Management, 49: 73-93, 1997.
10. T. Denoeux, A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory, IEEE Transactions on Systems, Man, and Cybernetics, 25(5), 1995, 804-813.
11. A. Appriou, Multisensor signal processing in the framework of the theory of evidence, Application of Mathematical Signal Processing Techniques to Mission Systems. November 1999, pp. 5-31, Research and Technology Organization (Lecture Series 216).
12. E. Lefevre, O. Colot, P. Vannoorenberghe, and D de Brucq, Knowledge modeling methods in the framework of belief theory, IEEE Conference on Systems, Man and Cybernetics, SMC'2000, Nashville, Tennessee, USA, 8-11 October 2000, 2806-2811.
13. G. Shafer, A mathematical Theory of Evidence (Princeton Univ Press., Princeton New Jersey, 1976).
14. P. Smets, Constructing the pignistic probability function in a context of uncertainty, Uncertainty in Artificial. Intelligence 5, M. Henrion, R. D. scha 1990.
15. T. Denoeux, Analysis of evidence-theory decision rules for pattern classification, Pattern Recognition, 30(7), 1997, 1095-1107.
16. L. M. Zouhal and T. Denoeux, An evidence-theoretic k-NN rule with parameter optimization. IEEE Transactions on Systems, Man and Cybernetics - Part C, 28(2), 1998, 263-273.