A New Belief Entropy Based on Deng Entropy [PDF]
Entropy, 2019 For Dempster−Shafer evidence theory, how to measure the uncertainty of basic probability assignment (BPA) is still an open question. Deng entropy is one of the methods for measuring the uncertainty of Dempster−Shafer evidence.
Dan Wang, Jiale Gao, Daijun Wei
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A modified belief entropy in Dempster-Shafer framework. [PDF]
PLoS ONE, 2017 How to quantify the uncertain information in the framework of Dempster-Shafer evidence theory is still an open issue. Quite a few uncertainty measures have been proposed in Dempster-Shafer framework, however, the existing studies mainly focus on the mass
Deyun Zhou, Yongchuan Tang, Wen Jiang
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Belief Entropy Tree and Random Forest: Learning from Data with Continuous Attributes and Evidential Labels [PDF]
Entropy, 2022 As well-known machine learning methods, decision trees are widely applied in classification and recognition areas. In this paper, with the uncertainty of labels handled by belief functions, a new decision tree method based on belief entropy is proposed ...
Kangkai Gao, Yong Wang, Liyao Ma
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A New Belief Entropy in Dempster–Shafer Theory Based on Basic Probability Assignment and the Frame of Discernment [PDF]
Entropy, 2020 Dempster–Shafer theory has been widely used in many applications, especially in the measurement of information uncertainty. However, under the D-S theory, how to use the belief entropy to measure the uncertainty is still an open issue.
Jiapeng Li, Qian Pan
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An Improved Belief Entropy in Evidence Theory
IEEE Access, 2020 Uncertainty measurement of the basic probability assignment function has always been a hot issue in Dempster-Shafer evidence. Many existing studies mainly consider the influence of the mass function itself and the size of the frame of discernment, so ...
Hangyu Yan, Yong Deng
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Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory [PDF]
Entropy, 2020 Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption.
Bin Yang +3 more
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Uncertainty Management in Assessment of FMEA Expert Based on Negation Information and Belief Entropy [PDF]
Entropy, 2023 The failure mode and effects analysis (FMEA) is a commonly adopted approach in engineering failure analysis, wherein the risk priority number (RPN) is utilized to rank failure modes. However, assessments made by FMEA experts are full of uncertainty.
Lei Wu +3 more
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Multi-Source Information Fusion Based on Negation of Reconstructed Basic Probability Assignment with Padded Gaussian Distribution and Belief Entropy [PDF]
Entropy, 2022 Multi-source information fusion is widely used because of its similarity to practical engineering situations. With the development of science and technology, the sources of information collected under engineering projects and scientific research are more
Yujie Chen +3 more
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An Improved Belief Entropy and Its Application in Decision-Making [PDF]
Complexity, 2017 Uncertainty measure in data fusion applications is a hot topic; quite a few methods have been proposed to measure the degree of uncertainty in Dempster-Shafer framework.
Deyun Zhou, Yongchuan Tang, Wen Jiang
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An Intuitionistic Evidential Method for Weight Determination in FMEA Based on Belief Entropy [PDF]
Entropy, 2019 Failure Mode and Effects Analysis (FMEA) has been regarded as an effective analysis approach to identify and rank the potential failure modes in many applications.
Zeyi Liu, Fuyuan Xiao
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