The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximations for efficient computation in the theory of evidence
Artificial Intelligence
Artificial Intelligence
A framework for multi-source data fusion
Information Sciences: an International Journal - Special issue: Soft computing data mining
On transformations of belief functions to probabilities: Research Articles
International Journal of Intelligent Systems - Uncertainty Processing
Analyzing the combination of conflicting belief functions
Information Fusion
Robust combination rules for evidence theory
Information Fusion
Decision fusion for postal address recognition using belief functions
Expert Systems with Applications: An International Journal
Analyzing the degree of conflict among belief functions
Artificial Intelligence
Credal semantics of Bayesian transformations in terms of probability intervals
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Assessing sensor reliability for multisensor data fusion within the transferable belief model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Two New Bayesian Approximations of Belief Functions Based on Convex Geometry
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
How to preserve the conflict as an alarm in the combination of belief functions?
Decision Support Systems
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In the framework of belief functions, information fusion is based on the construction of a unique belief function resulting from the combination of available belief functions induced from several information sources. When sources are reliable and distinct, Smets' conjunctive rule, which is equivalent to Dempster's rule of combination without the normalization process, can be considered. This rule offers interesting properties, but in return the empty set is an absorbing element: a series of conjunctive combinations tends to bring a mass equal to 1 to the empty set, making impossible the distinction between a real problem and an effect due to this absorbing effect of the empty set. Then a formalism allowing the preservation of the conflict which reflects the opposition between sources, is introduced in this paper. Based on the normalization process and on distance measures between belief functions, it is tested and compared with classic conjunctive operators on synthetic belief functions.