Journal ArticleDOI
A new divergence measure for belief functions in D–S evidence theory for multisensor data fusion
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TLDR
The proposed RB divergence is the first such measure to consider the correlations between both belief functions and subsets of the sets of belief functions, thus allowing it to provide a more convincing and effective solution for measuring the discrepancy between BBAs in D–S evidence theory.About:
This article is published in Information Sciences.The article was published on 2020-04-01. It has received 177 citations till now. The article focuses on the topics: Divergence (statistics) & Measure (mathematics).read more
Citations
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Journal ArticleDOI
Uncertainty measure in evidence theory
TL;DR: The development of Deng entropy as an effective way to measure uncertainty, including introducing its definition, analyzing its properties, and comparing it to other measures are discussed, and the challenges for future studies on uncertainty measurement in evidence theory are examined.
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Generalization of Dempster–Shafer theory: A complex mass function
TL;DR: A generalized Dempster–Shafer evidence theory is proposed, which provides a promising way to model and handle more uncertain information and an algorithm for decision-making is devised based on this theory.
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A Novel Conflict Measurement in Decision-Making and Its Application in Fault Diagnosis
TL;DR: This article proposes a novel evidential correlation coefficient (ECC) for belief functions by measuring the conflict between two pieces of evidence in decision making and investigates the properties of the ECC and conflict coefficients, which are all proven to satisfy the desirable properties for conflict measurement.
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Conflicting evidence combination from the perspective of networks
TL;DR: This paper study the combination of evidences from the perspective of networks: BOEs are regarded as nodes, the conflicting degree between BOEs is considered as one possible interaction between nodes, and the modified BOEs can be efficiently combined by Dempster’s rule of combination.
Journal ArticleDOI
CED: A Distance for Complex Mass Functions
TL;DR: A generalized evidential distance measure called the CED is proposed, which can measure the difference or dissimilarity between CBBAs in complex evidence theory and is applied to a medical diagnosis problem to illustrate its practicability.
References
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Book
A mathematical theory of evidence
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
Book ChapterDOI
Upper and Lower Probabilities Induced by a Multivalued Mapping
TL;DR: A distinctive feature of the present approach is a rule for conditioning, or more generally, arule for combining sources of information, as discussed in Sects.
Upper and Lower Probabilities Induced by a Multivalued Mapping.
TL;DR: In this paper, a multivalued mapping from a space X to a space S carries a probability measure defined over subsets of X into a system of upper and lower probabilities over S. Some basic properties of such systems are explored in Sects. 1 and 2.
Journal ArticleDOI
Multisensor data fusion: A review of the state-of-the-art
TL;DR: A comprehensive review of the data fusion state of the art is proposed, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies.
Journal ArticleDOI
Combining belief functions when evidence conflicts
TL;DR: To achieve convergence, this research suggests incorporating average belief into the combining rule, which best solves the normalization problems, but it does not offer convergence toward certainty, nor a probabilistic basis.