On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
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
Combining belief functions when evidence conflicts
Decision Support Systems
The consensus operator for combining beliefs
Artificial Intelligence
Combining belief functions based on distance of evidence
Decision Support Systems
Analyzing the combination of conflicting belief functions
Information Fusion
A new combination of evidence based on compromise
Fuzzy Sets and Systems
Robust combination rules for evidence theory
Information Fusion
Decision fusion for postal address recognition using belief functions
Expert Systems with Applications: An International Journal
The canonical decomposition of a weighted belief
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Analyzing the degree of conflict among belief functions
Artificial Intelligence
Cumulative and averaging fusion of beliefs
Information Fusion
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
Discountings of a Belief Function Using a Confusion Matrix
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Conflict management in Dempster--Shafer theory using the degree of falsity
International Journal of Approximate Reasoning
Towards an alarm for opposition conflict in a conjunctive combination of belief functions
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Evidential calibration process of multi-agent based system: An application to forensic entomology
Expert Systems with Applications: An International Journal
Singular sources mining using evidential conflict analysis
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
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
The modified Dempster-Shafer approach to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In the belief function framework, a unique function is induced from the use of a combination rule so allowing to synthesize all the knowledge of the initial belief functions. When information sources are reliable and independent, the conjunctive rule of combination, proposed by Smets, may be used. This rule is equivalent to the Dempster rule without the normalization process. The conjunctive combination provides interesting properties, as the commutativity and the associativity. However, it is characterized by having the empty set, called also the conflict, as an absorbing element. So, when we apply a significant number of conjunctive combinations, the mass assigned to the conflict tends to 1 which makes impossible returning the distinction between the problem arisen during the fusion and the effect due to the absorption power of the empty set. The objective of this paper is then to define a formalism preserving the initial role of the conflict as an alarm signal announcing that there is a kind of disagreement between sources. More exactly, that allows to preserve some conflict, after the fusion by keeping only the part of conflict reflecting the opposition between the belief functions. This approach is based on dissimilarity measures and on a normalization process between belief functions. Our proposed formalism is tested and compared with the conjunctive rule of combination on synthetic belief functions.