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
On the justification of Dempster's rule of combination
Artificial Intelligence
Two views of belief: belief as generalized probability and belief as evidence
Artificial 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
Fusion rules for merging uncertain information
Information Fusion
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Combining Uncertain Outputs from Multiple Ontology Matchers
SUM '07 Proceedings of the 1st international conference on Scalable Uncertainty Management
On the Definition of Essential and Contingent Properties of Subjective Belief Bases
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
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
A model for the integration of prioritized knowledge bases through subjective belief games
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Expert Systems with Applications: An International Journal
Belief functions combination without the assumption of independence of the information sources
International Journal of Approximate Reasoning
On consistent approximations of belief functions in the mass space
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
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
Expert Systems with Applications: An International Journal
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
Measuring conflict between possibilistic uncertain information through belief function theory
KSEM'06 Proceedings of the First international conference on Knowledge Science, Engineering and Management
The impact of diversity on the accuracy of evidential classifier ensembles
International Journal of Approximate Reasoning
A characteristic function approach to inconsistency measures for knowledge bases
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
A Model for Decision Making with Missing, Imprecise, and Uncertain Evaluations of Multiple Criteria
International Journal of Intelligent Systems
A belief function distance metric for orderable sets
Information Fusion
Selecting source behavior in information fusion on the basis of consistency and specificity
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Integrating textual analysis and evidential reasoning for decision making in Engineering design
Knowledge-Based Systems
Environmental impact assessment based on D numbers
Expert Systems with Applications: An International Journal
Information-based dissimilarity assessment in Dempster-Shafer theory
Knowledge-Based Systems
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
Engineering Applications of Artificial Intelligence
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The study of alternative combination rules in DS theory when evidence is in conflict has emerged again recently as an interesting topic, especially in data/information fusion applications. These studies have mainly focused on investigating which alternative would be appropriate for which conflicting situation, under the assumption that a conflict is identified. The issue of detection (or identification) of conflict among evidence has been ignored. In this paper, we formally define when two basic belief assignments are in conflict. This definition deploys quantitative measures of both the mass of the combined belief assigned to the emptyset before normalization and the distance between betting commitments of beliefs. We argue that only when both measures are high, it is safe to say the evidence is in conflict. This definition can be served as a prerequisite for selecting appropriate combination rules.