A New Belief Entropy to Measure Uncertainty of Basic Probability Assignments Based on Belief Function and Plausibility Function [PDF]
Entropy, 2018 How to measure the uncertainty of the basic probability assignment (BPA) function is an open issue in Dempster⁻Shafer (D⁻S) theory.
Lipeng Pan, Yong Deng
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Improved Base Belief Function-Based Conflict Data Fusion Approach Considering Belief Entropy in the Evidence Theory [PDF]
Entropy, 2020 Due to the nature of the Dempster combination rule, it may produce results contrary to intuition. Therefore, an improved method for conflict evidence fusion is proposed.
Shuang Ni, Yan Lei, Yongchuan Tang
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Machine Learning Algorithms and Fault Detection for Improved Belief Function Based Decision Fusion in Wireless Sensor Networks [PDF]
Sensors, 2019 Decision fusion is used to fuse classification results and improve the classification accuracy in order to reduce the consumption of energy and bandwidth demand for data transmission.
Atia Javaid +6 more
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Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks [PDF]
Sensors, 2015 Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem
Wenyu Zhang, Zhenjiang Zhang
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A new weighting factor in combining belief function. [PDF]
PLoS ONE, 2017 Dempster-Shafer evidence theory has been widely used in various applications. However, to solve the problem of counter-intuitive outcomes by using classical Dempster-Shafer combination rule is still an open issue while fusing the conflicting evidences ...
Deyun Zhou +6 more
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Mathematics, 2020 The Dempster–Shafer evidence theory has been widely applied in the field of information fusion. However, when the collected evidence data are highly conflicting, the Dempster combination rule (DCR) fails to produce intuitive results most of the time.
Dingyi Gan, Bin Yang, Yongchuan Tang
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Negation of Belief Function Based on the Total Uncertainty Measure [PDF]
Entropy, 2019 The negation of probability provides a new way of looking at information representation. However, the negation of basic probability assignment (BPA) is still an open issue.
Kangyang Xie, Fuyuan Xiao
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A New Distance Measure of Belief Function in Evidence Theory [PDF]
IEEE Access, 2019 How to measure the similarity or distance between the basic probability assignment (BPA) in evidence theory is an open issue. The existing evidence distance function has the shortcoming that the cardinality of each subset is not reasonably considered. To
Cuiping Cheng, Fuyuan Xiao
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Sensor Reliability Evaluation Scheme for Target Classification Using Belief Function Theory [PDF]
Sensors, 2013 In the target classification based on belief function theory, sensor reliability evaluation has two basic issues: reasonable dissimilarity measure among evidences, and adaptive combination of static and dynamic discounting. One solution to the two issues
Jing Zhu, Yupin Luo, Jianjun Zhou
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Conflict management based on belief function entropy in sensor fusion. [PDF]
Springerplus, 2016 Yuan K, Xiao F, Fei L, Kang B, Deng Y.
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