Entropy and Specificity in a Mathematical Theory of Evidence

By Ronald R. Yager. Classic Works of the Dempster-Shafer Theory of Belief Functions, 2008

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Analyzing the degree of conflict among belief functions (2006, methodology): “In the past, apart from using m⊕(∅) as the quantitative measure of conflict among two bbas, some other methods were proposed to measure the conflict or confusion or non-specificity in a single belief function, such as [3,5,8,12,17, 25].”
Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy (2019, methodology): “As the generalization of the Shannon entropy [86,87], the Deng entropy is an efficient method to measure the uncertain information.”
Analyzing the combination of conflicting belief functions (2007, background): “Still [48] presents a better justification based on the concept of specialization introduced in [109] [51].”
Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (2019, background): “Even more interestingly, a basic distinction between two types of uncertainty contained in a credal set, referred to as conflict (randomness, discord) and non-specificity, respectively, has been made by Yager (1983).”