Knowledge modeling methods in the framework of evidence theory: an experimental comparison for melanoma detection

The Dempster-Shafer theory, or evidence theory, is used in different fields such as data fusion, regression or classification. Within the framework of this theory, uncertain and imprecise data are represented using belief functions. Data fusion operators as well as the decision rule of this theory were largely developed and formalized. The aim of the paper is to present modeling methods of knowledge for the initialization of belief functions. Moreover, an experimental comparison of these different modeling methods on real data extracted from images of dermatological lesions is presented.

[1]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[3]  Thierry Denoeux,et al.  An Adaptive k-NN Rule Based on Dempster-Shafer Theory , 1995, CAIP.

[4]  Philippe Smets,et al.  Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem , 1993, Int. J. Approx. Reason..

[5]  Thierry Denoeux,et al.  Analysis of evidence-theoretic decision rules for pattern classification , 1997, Pattern Recognit..

[6]  E. Lefevre,et al.  Using information criteria in Dempster-Shafer's basic belief assignment , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[7]  O. Colot,et al.  A generic framework for resolving the conflict in the combination of belief structures , 2000, Proceedings of the Third International Conference on Information Fusion.

[8]  Philippe Smets,et al.  The Transferable Belief Model , 1994, Artif. Intell..

[9]  Henri Prade,et al.  Representation and combination of uncertainty with belief functions and possibility measures , 1988, Comput. Intell..

[10]  Lotfi A. Zadeh,et al.  On the Validity of Dempster''s Rule of Combination of Evidence , 1979 .

[11]  Thierry Denoeux,et al.  A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[12]  Philippe Smets,et al.  Constructing the Pignistic Probability Function in a Context of Uncertainty , 1989, UAI.