The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Road extraction from multi-temporal satellite images by an evidential reasoning approach
Pattern Recognition Letters
The dynamic of belief in the transferable belief model and specialization-generalization matrices
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Artificial Intelligence
Fuzzy sets as a basis for a theory of possibility
Fuzzy Sets and Systems
ECM: An evidential version of the fuzzy c-means algorithm
Pattern Recognition
A definition of subjective possibility
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Belief functions on real numbers
International Journal of Approximate Reasoning
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hierarchical and conditional combination of belief functions induced by visual tracking
International Journal of Approximate Reasoning
Independence concepts in evidence theory
International Journal of Approximate Reasoning
Compositional models and conditional independence in evidence theory
International Journal of Approximate Reasoning
Belief functions combination without the assumption of independence of the information sources
International Journal of Approximate Reasoning
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
Singular sources mining using evidential conflict analysis
International Journal of Approximate Reasoning
Belief functions contextual discounting and canonical decompositions
International Journal of Approximate Reasoning
The conjunctive combination of interval-valued belief structures from dependent sources
International Journal of Approximate Reasoning
Theory of evidence for face detection and tracking
International Journal of Approximate Reasoning
Expert Systems with Applications: An International Journal
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The combination rule is critical in an evidence based fusion process. The conjunctive rule is most common eventhough when the cognitive independence - distinctness - assumption is often questionable. A new combination rule is tested here in both discrete and continuous cases, accounting for a partial non-distinctness between evidences. It is based on 'generalized discounting', that we define for separable basic belief assignments (bbas) or basic belief densities (bbds), to be applied to the source correlation derived from the cautious rule. This correlation can be specified in both considered cases of consonant bbas/bbds (as proposed by Dubois et al.) and separable bbas/bbds (as proposed by Denoeux). Then, the so-called 'cautious-adaptive' rule varies between the conjunctive rule and the cautious one, depending on the discounting level. In the Gaussian case with standard deviation @s, the evidence non-distinctness will be parameterized by a factor @r@?[0,1] dividing @s. It leads to the generalized discounting needed in the cautious-adaptive formulation.