On considerations of credibility of evidence
International Journal of Approximate Reasoning
Approximations for efficient computation in the theory of evidence
Artificial Intelligence
The transferable belief model and other interpretations of Dempster-Shafer's model
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Conflicts within and between belief functions
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Belief function theory provides a robust framework for uncertain information modeling. It also offers several fusion tools in order to profit from multi-source context. Nevertheless, fusion is a sensible task where conflictual information may appear especially when sources are unreliable. Therefore, measuring source's reliability has been the center of many research and development. Existing solutions for source's reliability estimation are based on the assumption that distance is the only factor for conflictual situations. Indeed, integrating only distance measures to estimate source's reliability is not sufficient where source's confusion may be also considered as conflict origin. In this paper, we tackle reliability estimation and we introduce a new discounting operator that considers those two possible conflict origins. We propose an automatic method for discounting factor calculation. Those factors are integrated on belief classifier and tested on high-resolution image classification problem.