On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
On the justification of Dempster's rule of combination
Artificial Intelligence
Artificial Intelligence
The alpha-junctions: Combination Operators Applicable to Belief Functions
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Analyzing the combination of conflicting belief functions
Information Fusion
Correspondence: Comments on “A new combination of evidence based on compromise” by K. Yamada
Fuzzy Sets and Systems
Hierarchical and conditional combination of belief functions induced by visual tracking
International Journal of Approximate Reasoning
Consonant continuous belief functions conflicts calculation
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Multi-dimensional evidence-based trust management with multi-trusted paths
Future Generation Computer Systems
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
Theory of evidence for face detection and tracking
International Journal of Approximate Reasoning
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
A belief function distance metric for orderable sets
Information Fusion
Evidential reasoning rule for evidence combination
Artificial Intelligence
Expert Systems with Applications: An International Journal
How to preserve the conflict as an alarm in the combination of belief functions?
Decision Support Systems
Hi-index | 0.00 |
Dempster's rule of combination in evidence theory is a powerful tool for reasoning under uncertainty. Since Zadeh highlighted the counter-intuitive behaviour of Dempster's rule, a plethora of alternative combination rules have been proposed. In this paper, we propose a general formulation for combination rules in evidence theory as a weighted sum of the conjunctive and disjunctive rules. Moreover, with the aim of automatically accounting for the reliability of sources of information, we propose a class of robust combination rules (RCR) in which the weights are a function of the conflict between two pieces of information. The interpretation given to the weight of conflict between two BPAs is an indicator of the relative reliability of the sources: if the conflict is low, then both sources are reliable, and if the conflict is high, then at least one source is unreliable. We show some interesting properties satisfied by the RCRs, such as positive belief reinforcement or the neutral impact of vacuous belief, and establish links with other classes of rules. The behaviour of the RCRs over non-exhaustive frames of discernment is also studied, as the RCRs implicitly perform a kind of automatic deconditioning through the simple use of the disjunctive operator. We focus our study on two special cases: (1) RCR-S, a rule with symmetric coefficients that is proved to be unique and (2) RCR-L, a rule with asymmetric coefficients based on a logarithmic function. Their behaviours are then compared to some classical combination rules proposed thus far in the literature, on a few examples, and on Monte Carlo simulations.