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A three-way density peak clustering method based on evidence theory
Knowledge-Based Systems, 2021Density peaks clustering (DPC) algorithm is an efficient and simple clustering method attracting the attention of many researchers. However, its strategy of assigning each non-grouped object to the same cluster depends on its nearest neighbors having a ...
Hui Yu, Luyuan Chen, Jingtao Yao
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A Mathematical Theory of Evidence
A Mathematical Theory of Evidence, 2020Both in science and in practical affairs we reason by combining facts only inconclusively supported by evidence. Building on an abstract understanding of this process of combination, this book constructs a new theory of epistemic probability.
G. Shafer
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A belief Hellinger distance for D-S evidence theory and its application in pattern recognition
Engineering applications of artificial intelligence, 2021Dempster–Shafer (D–S) evidence theory has been studied and applied broadly, owing to its advantage of effectively handling uncertainty problems in multisource information fusion. But under the circumstance of the body of evidences are highly conflicting,
Chaosheng Zhu, Fuyuan Xiao
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Quantum Pythagorean Fuzzy Evidence Theory: A Negation of Quantum Mass Function View
IEEE transactions on fuzzy systems, 2021Dempster–Shafer (D-S) evidence theory is an effective methodology to handle unknown and imprecise information because it can assign probability into the power set.
Xiaozhuan Gao, Lipeng Pan, Yong Deng
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Cross-Domain Pattern Classification With Distribution Adaptation Based on Evidence Theory
IEEE Transactions on Cybernetics, 2021In pattern classification, there may not exist labeled patterns in the target domain to train a classifier. Domain adaptation (DA) techniques can transfer the knowledge from the source domain with massive labeled patterns to the target domain for ...
Linqing Huang, Zhun-ga Liu, J. Dezert
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A new divergence measure for belief functions in D-S evidence theory for multisensor data fusion
Information Sciences, 2020Dempster–Shafer (D–S) evidence theory is useful for handling uncertainty problems in multisensor data fusion. However, the question of how to handle highly conflicting evidence in D–S evidence theory is still an open issue.
Fuyuan Xiao
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A Transfer Classification Method for Heterogeneous Data Based on Evidence Theory
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021It remains a challenging problem for data classification without training patterns. In many applications, there may exist some labeled data in other related domains (called source domain), and such labeled data can be helpful to solve the classification ...
Zhunga Liu +3 more
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Evidence-Theory-Based Reliability Analysis Through Kriging Surrogate Model
Journal of Mechanical Design, 2021It is generally understood that intractable computational intensity stemming from repeatedly calling performance function when evaluating the contribution of joint focal elements hinders the application of evidence theory in practical engineering.
Dequan Zhang +4 more
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IEEE transactions on fuzzy systems, 2020
A novel risk analysis approach is developed by merging interval-valued fuzzy sets (IVFSs), improved Dempster–Shafer (D–S) evidence theory, and fuzzy Bayesian networks (BNs), acting as a systematic decision support approach for safety insurance for the ...
Yue Pan, Limao Zhang, Zhiwu Li, L. Ding
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A novel risk analysis approach is developed by merging interval-valued fuzzy sets (IVFSs), improved Dempster–Shafer (D–S) evidence theory, and fuzzy Bayesian networks (BNs), acting as a systematic decision support approach for safety insurance for the ...
Yue Pan, Limao Zhang, Zhiwu Li, L. Ding
semanticscholar +1 more source
MMGET: a Markov model for generalized evidence theory
Computational and Applied Mathematics, 2021In real life, lots of information merge from time to time. To appropriately describe actual situations in open world, a generalized evidence theory based on Dempster–Shafer evidence theory is designed. However, everything occurs in sequence and owns some
Yuanpeng He, Yong Deng
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