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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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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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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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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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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Belief functions on lattices [PDF]
International Journal of Intelligent Systems, 2008 We extend the notion of belief function to the case where the underlying structure is no more the Boolean lattice of subsets of some universal set, but any lattice, which we will endow with a minimal set of properties according to our needs. We show that
Grabisch, Michel
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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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Feature Selection for Interval-Valued Data Based on D-S Evidence Theory
IEEE Access, 2021 Feature selection is one basic and critical technology for data mining, especially in current “big data era”. Rough set theory (RST) is sensitive to noise in feature selection due to the strict condition of equivalence relation.
Yichun Peng, Qinli Zhang
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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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