Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Analyzing the combination of conflicting belief functions
Information Fusion
Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Dynamic Reduct from Partially Uncertain Data Using Rough Sets
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Semigroup structure of singleton Dempster-Shafer evidence accumulation
IEEE Transactions on Information Theory
A comparison of dynamic and static belief rough set classifier
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Rule discovery process based on rough sets under the belief function framework
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Expert Systems with Applications: An International Journal
Classification with dynamic reducts and belief functions
Transactions on rough sets XIV
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
Clustering approach using belief function theory
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Heuristic for attribute selection using belief discernibility matrix
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
A belief function distance metric for orderable sets
Information Fusion
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
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This paper describes “modified Dempster-Shafer” (MDS), an approach to object identification which incorporates Bayesian prior distributions into an altered Dempster-Shafer rule of combination. The MDS combination rule reduces, under strong independence assumptions, to a special case of Bayes' rule. We show that MDS has rigorous probabilistic foundations in the theory of random sets. We also demonstrate close relationships between MDS and Smets' “pignistic” probabilities (1990), which in the MDS framework become true posterior distributions. We describe the application of MDS to a practical classification algorithm which uses an information-theoretic technique to limit the combinatorial explosion of evidence. We also define a non-ad hoc, MDS-based classification “miss distance” metric used to measure the performance of this algorithm