Readings in uncertain reasoning
Readings in uncertain reasoning
Combining belief functions when evidence conflicts
Decision Support Systems
Combining belief functions based on distance of evidence
Decision Support Systems
Analyzing the combination of conflicting belief functions
Information Fusion
Analyzing the degree of conflict among belief functions
Artificial Intelligence
Decision making in the TBM: the necessity of the pignistic transformation
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
Linear utility theory for belief functions
Operations Research Letters
A new belief-based K-nearest neighbor classification method
Pattern Recognition
Integrating textual analysis and evidential reasoning for decision making in Engineering design
Knowledge-Based Systems
Evidential classifier for imprecise data based on belief functions
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
How to preserve the conflict as an alarm in the combination of belief functions?
Decision Support Systems
A belief classification rule for imprecise data
Applied Intelligence
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The sources of evidence may have different reliability and importance in real applications for decision making. The estimation of the discounting (weighting) factors when the prior knowledge is unknown have been regularly studied until recently. In the past, the determination of the weighting factors focused only on reliability discounting rule and it was mainly dependent on the dissimilarity measure between basic belief assignments (bba's) represented by an evidential distance. Nevertheless, it is very difficult to characterize efficiently the dissimilarity only through an evidential distance. Thus, both a distance and a conflict coefficient based on probabilistic transformations BetP are proposed to characterize the dissimilarity. The distance represents the difference between bba's, whereas the conflict coefficient reveals the divergence degree of the hypotheses that two belief functions strongly support. These two aspects of dissimilarity are complementary in a certain sense, and their fusion is used as the dissimilarity measure. Then, a new estimation method of weighting factors is presented by using the proposed dissimilarity measure. In the evaluation of weight of a source, both its dissimilarity with other sources and their weighting factors are considered. The weighting factors can be applied in the both importance and reliability discounting rules, but the selection of the adapted discounting rule should depend on the actual application. Simple numerical examples are given to illustrate the interest of the proposed approach.