Constructing the Pignistic Probability Function in a Context of Uncertainty
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
International Journal of Approximate Reasoning
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Geometric Approach to the Theory of Evidence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
A belief function distance metric for orderable sets
Information Fusion
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In this paper we provide a proof for the positive definiteness of the Jaccard index matrix used as a weighting matrix in the Euclidean distance between belief functions defined in Jousselme et al. [13]. The idea of this proof relies on the decomposition of the matrix into an infinite sum of positive semidefinite matrices. The proof is valid for any size of the frame of discernment but we provide an illustration for a frame of three elements. The Jaccard index matrix being positive definite guaranties that the associated Euclidean distance is a full metric and thus that a null distance between two belief functions implies that these belief functions are strictly identical.