Weights of evidence and internal conflict for support functions
Information Sciences: an International Journal
On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining opinions from several experts
Applied Artificial Intelligence
Perspectives on the theory and practice of belief functions
International Journal of Approximate Reasoning
Reasoning with belief functions: an analysis of compatibility
International Journal of Approximate Reasoning
On the justification of Dempster's rule of combination
Artificial Intelligence
Artificial Intelligence
What is Dempster-Shafer's model?
Advances in the Dempster-Shafer theory of evidence
Representation, independence, and combination of evidence in the Dempster-Shafer theory
Advances in the Dempster-Shafer theory of evidence
Focusing versus updating in belief function theory
Advances in the Dempster-Shafer theory of evidence
Combining belief functions when evidence conflicts
Decision Support Systems
The consensus operator for combining beliefs
Artificial Intelligence
Logical method for logical operations based on evidential reasoning
International Journal of Knowledge Engineering and Soft Data Paradigms
Correspondence: Comments on “A new combination of evidence based on compromise” by K. Yamada
Fuzzy Sets and Systems
Belief Logic Programming: Uncertainty Reasoning with Correlation of Evidence
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
On the fusion of imprecise uncertainty measures using belief structures
Information Sciences: an International Journal
A new evidential trust model for open distributed 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
Sequential weighted combination for unreliable evidence based on evidence variance
Decision Support Systems
A choice model with imprecise ordinal evaluations
International Journal of Approximate Reasoning
Joint cumulative distribution functions for Dempster---Shafer belief structures using copulas
Fuzzy Optimization and Decision Making
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Dempster rule of combination is the standard way of combining multiple pieces of evidence given by independent sources of information. However, it aroused many controversies about its validity, and many alternatives have been proposed. The paper examines the model of combination in Dempster's original paper and indicates that handling of the independence required among multiple pieces of evidence is strange from the viewpoint of semantics, where the independence among occurrences of multiple pieces of information might be confused with the consistency among contents of the information. The paper then proposes a new model of combination and a new rule of combination called combination by compromise as a consensus generator. The properties of the proposed combination as well as several alternative combination methods proposed so far are discussed in the light of the drawbacks and advantages of Dempster rule. Several numerical examples which demonstrate the properties are also shown. The discussion and the examples suggest that the proposed combination produces the most preferable results among them from the viewpoints of consensus generation.