Measuring Consensus Effectiveness by a Generalized Entropy Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Consonant approximation of belief functions
International Journal of Approximate Reasoning
A note on the measure of discord
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Approximations for efficient computation in the theory of evidence
Artificial Intelligence
Uncertainty measures for evidential reasoning. II: A new measure of total uncertainty
International Journal of Approximate Reasoning
Inner and outer approximation of belief structures using a hierarchical clustering approach
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Fast Algorithms for Dempster-Shafer Theory
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Handling Different Forms of Uncertainty in Regression Analysis: A Fuzzy Belief Structure Approach
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Uncertainty and Information: Foundations of Generalized Information Theory
Uncertainty and Information: Foundations of Generalized Information Theory
Clustering decomposed belief functions using generalized weights of conflict
International Journal of Approximate Reasoning
Fuzzy Information Fusion Algorithm of Fault Diagnosis Based on Similarity Measure of Evidence
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Analyzing the degree of conflict among belief functions
Artificial Intelligence
An information systems security risk assessment model under uncertain environment
Applied Soft Computing
International Journal of Approximate Reasoning
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
A Geometric Approach to the Theory of Evidence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
EVCLUS: evidential clustering of proximity data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The modified Dempster-Shafer approach to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Measuring ambiguity in the evidence theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Remarks on “Measuring Ambiguity in the Evidence Theory”
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Dissimilarity assessment is a central problem in the Dempster-Shafer Theory (DST), where the difference in information content between two bodies of evidence (BoEs) should be quantified. Different dissimilarity measures (DMs) have been proposed; however, no single DM seems to be comprehensive enough to compare all aspects of information conveyed by BoEs. The information content of DMs are highly correlated as well. In this paper, DMs are categorized based on their interpretation of information content, emphasizing entropy-like DMs. A methodology is then proposed to select a set of more informative and less overlapping DMs called the ''set of most discriminative dissimilarity measures'' (smDDM). A forward selection procedure based on an appropriate criterion was utilized and the threshold for selection was derived naturally. To enhance the numerical evaluation, two experimental setups were designed and utilized with the existing setup to provide a sample of dissimilarity values. Comprehensive analysis supports the favorable properties of the proposed smDDM. The selected DMs came naturally from six different categories and subcategories of inner product-based and entropy-like DMs. Optimality analysis shows that the proposed selection procedure resulted in an appropriate near-optimal solution. Dissimilarity assessment is an integrated part of many applications of DST. The applicability and performance of the smDDM was examined and verified for two case studies: evidential clustering and sensor reliability evaluation.