Uncertainty measures for evidential reasoning. II: A new measure of total uncertainty
International Journal of Approximate Reasoning
Machine Learning
Upper and Lower Entropies of Belief Functions Using Compatible Probability Functions
ISMIS '93 Proceedings of the 7th International Symposium on Methodologies for Intelligent Systems
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
Analyzing the combination of conflicting belief functions
Information Fusion
The Dempster--Shafer calculus for statisticians
International Journal of Approximate Reasoning
Upper entropy of credal sets. Applications to credal classification
International Journal of Approximate Reasoning
Measures of uncertainty for imprecise probabilities: An axiomatic approach
International Journal of Approximate Reasoning
Conflict management in Dempster--Shafer theory using the degree of falsity
International Journal of Approximate Reasoning
Evidence Combination in an Environment With Heterogeneous Sources
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Reducing Algorithm Complexity for Computing an Aggregate Uncertainty Measure
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Notes on “Reducing Algorithm Complexity for Computing an Aggregate Uncertainty Measure”
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We construct alternative frames of discernment from input belief functions. We assume that the core of each belief function is a subset of a so far unconstructed frame of discernment. The alternative frames are constructed as different cross products of unions of different cores. With the frames constructed the belief functions are combined for each alternative frame. The appropriateness of each frame is evaluated in two ways: (i) we measure the aggregated uncertainty (an entropy measure) of the combined belief functions for that frame to find if the belief functions are interacting in interesting ways, (ii) we measure the conflict in Dempster's rule when combining the belief functions to make sure they do not exhibit too much internal conflict. A small frame typically yields a small aggregated uncertainty but a large conflict, and vice versa. The most appropriate frame of discernment is that which minimizes a probabilistic sum of the conflict and a normalized aggregated uncertainty of all combined belief functions for that frame of discernment.