Implementing Dempster's rule for hierarchial evidence
Artificial Intelligence
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Analyzing the combination of conflicting belief functions
Information Fusion
Combination of partially non-distinct beliefs: The cautious-adaptive rule
International Journal of Approximate Reasoning
Outlier Detection in Ad Hoc Networks Using Dempster-Shafer Theory
MDM '09 Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware
Hierarchical and conditional combination of belief functions induced by visual tracking
International Journal of Approximate Reasoning
Conflict management in Dempster--Shafer theory using the degree of falsity
International Journal of Approximate Reasoning
Belief functions contextual discounting and canonical decompositions
International Journal of Approximate Reasoning
Controlling Remanence in Evidential Grids Using Geodata for Dynamic Scene Perception
International Journal of Approximate Reasoning
How to preserve the conflict as an alarm in the combination of belief functions?
Decision Support Systems
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Singular sources mining is essential in many applications like sensor fusion or dataset analysis. A singular source of information provides pieces of evidence that are significantly different from the majority of the other sources. In the Dempster-Shafer theory, the pieces of evidence collected by a source are summarized by basic belief assignments (bbas). In this article, we propose to mine singular sources by analyzing the conflict between their corresponding bbas. By viewing the conflict as a function of parameters called discounting rates, new developments are obtained and a criterion that weights the contribution of each bba to the conflict is introduced. The efficiency and the robustness of this criterion is demonstrated on several sets of bbas with various specificities.