Weighted Conflict Evidence Combination Method Based on Hellinger Distance and the Belief Entropy

Li, Junwei; Xie, Baolin; Jin, Yong; Zhou, Lin

doi:10.1109/access.2020.3044605

Cited by 20 publications

(14 citation statements)

References 58 publications

(69 reference statements)

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“…where X is a FOD, |X | is the number of elements in X, A is the focal element of the mass function, [31] consider the scale of FOD and the intersection between evidences. However, when the scale of FOD is large, the influence factor of the number of conditions for the intersection between evidences will be greatly reduced for the measurement of the uncertainty of the evidence.…”

Section: The Proposed Uncertainty Measurementioning

confidence: 99%

“…For measuring the uncertainty of Dempster-Shafer evidence theory, Shannon entropy cannot be applied because a mass function is a generalized probability [23]. Therefore, some other methods are proposed for measuring the uncertainty of Dempster-Shafer evidence theory such as Dubois and Prade's weighted Hartley entropy [24], Hohle's confusion measure [25], Klir and Ramer's discord [26], Klir and Parviz's strife [27], Deng entropy [28], Pan and Deng's entropy [29], Cui et al's entropy [30] and Li et al's entropy [31]. However, for some cases, these entropy theories cannot effectively measure the uncertainty of Dempster-Shafer evidence theory.…”

Section: Introductionmentioning

confidence: 99%

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An Improved Method for Multisensor High Conflict Data Fusion

Wang

Bao

2021

Journal of Sensors

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Dempster-Shafer evidence theory can effectively process imperfect information and is widely used in a data fusion system. However, classical Dempster-Shafer evidence theory involves counter-intuitive behaviors with the data of multisensor high conflict in target identification system. In order to solve this problem, an improved evidence combination method is proposed in this paper. By calculating the support degree and the belief entropy of each sensor, the proposed method combines conflict evidences. A new method is used to calculate support degree in this paper. At the same time, inspired by Deng entropy, the modified belief entropy is proposed by considering the scale of the frame of discernment (FOD) and the relative scale of the intersection between evidences with respect to FOD. Because of these two modifications, the effect has been improved in conflict data fusion. Several methods are compared and analyzed through examples. And the result suggests the proposed method can not only obtain reasonable and correct results but also have the highest fusion reliability in solving the problem of high conflict data fusion.

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Section: The Proposed Uncertainty Measurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Improved Method for Multisensor High Conflict Data Fusion

Wang

Bao

2021

Journal of Sensors

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“…Finally, it validated the method by motor empirical cases under the decision rules. Li et al [ 36 ] proposed a weighted conflicting evidence combination method based on Hellinger distance and belief entropy., and uses distance to measure the conflict between evidence and applies belief entropy to quantify the uncertainty of basic belief assignments.…”

Section: Introductionmentioning

confidence: 99%

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

Gou

Zhang

et al. 2022

PLoS ONE

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In the process of intelligent system operation fault diagnosis and decision making, the multi-source, heterogeneous, complex, and fuzzy characteristics of information make the conflict, uncertainty, and validity problems appear in the process of information fusion, which has not been solved. In this study, we analyze the credibility and variation of conflict among evidence from the perspective of conflict credibility weight and propose an improved model of multi-source information fusion based on Dempster-Shafer theory (DST). From the perspectives of the weighting strategy and Euclidean distance strategy, we process the basic probability assignment (BPA) of evidence and assign the credible weight of conflict between evidence to achieve the extraction of credible conflicts and the adoption of credible conflicts in the process of evidence fusion. The improved algorithm weakens the problem of uncertainty and ambiguity caused by conflicts in the information fusion process, and reduces the impact of information complexity on analysis results. And it carries a practical application out with the fault diagnosis of wind turbine system to analyze the operation status of wind turbines in a wind farm to verify the effectiveness of the proposed algorithm. The result shows that under the conditions of improved distance metric evidence discrepancy and credible conflict quantification, the algorithm better shows the conflict and correlation among the evidence. It improves the accuracy of system operation reliability analysis, improves the utilization rate of wind energy resources, and has practical implication value.

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“…However, Murphy treats all the evidence with the same weight and does not take into account the relationships and difference among bodies of evidence, but not all evidence to be combined has equal significance in real applications. To better determine the weight of each piece of evidence, a series of methods have been published [3,5,[30][31][32][33]. The main difference between these methods is the way in which the weight of evidence is obtained, such as Deng et al's weight of evidence based on the evidence distance [30] and Yuan et al's entropybased weight [33].…”

Section: Introductionmentioning

confidence: 99%

“…Schubert dealt with conflicting evidence based on the degree of falsity [36]. Yu et al proposed a new supporting probability distance to discount conflicting evidence [31].…”

Section: Introductionmentioning

confidence: 99%

An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

2021

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Dempster–Shafer evidence theory is widely applied in various fields related to information fusion. However, the results are counterintuitive when highly conflicting evidence is fused with Dempster’s rule of combination. Many improved combination methods have been developed to address conflicting evidence. Nevertheless, all of these approaches have inherent flaws. To solve the existing counterintuitive problem more effectively and less conservatively, an improved combination method for conflicting evidence based on the redistribution of the basic probability assignment is proposed. First, the conflict intensity and the unreliability of the evidence are calculated based on the consistency degree, conflict degree and similarity coefficient among the evidence. Second, the redistribution equation of the basic probability assignment is constructed based on the unreliability and conflict intensity, which realizes the redistribution of the basic probability assignment. Third, to avoid excessive redistribution of the basic probability assignment, the precision degree of the evidence obtained by information entropy is used as the correction factor to modify the basic probability assignment for the second time. Finally, Dempster’s rule of combination is used to fuse the modified basic probability assignment. Several different types of examples and actual data sets are given to illustrate the effectiveness and potential of the proposed method. Furthermore, the comparative analysis reveals the proposed method to be better at obtaining the right results than other related methods.

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Weighted Conflict Evidence Combination Method Based on Hellinger Distance and the Belief Entropy

Cited by 20 publications

References 58 publications

An Improved Method for Multisensor High Conflict Data Fusion

An Improved Method for Multisensor High Conflict Data Fusion

Weighted assignment fusion algorithm of evidence conflict based on Euclidean distance and weighting strategy, and application in the wind turbine system

An improved conflicting-evidence combination method based on the redistribution of the basic probability assignment

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