An evidence combination rule based on a new weight assignment scheme

Wang, Yucui; Wang, Jian; Huang, Mengjie; Wang, Minghui

doi:10.1007/s00500-022-07160-2

Cited by 16 publications

(15 citation statements)

References 57 publications

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“…According to the maximum entropy principle, when BPA satisfies uniform distribution, the information entropy reaches the maximum, and the uncertainty also reaches the maximum. Wang, J [ 4 ] measured the degree of evidence fuzziness by calculating the distance between BPA and uniform distribution. The uniform distribution is defined as follows [ 4 ]:

where

represents the number of focal elements (the number of elements in the recognition framework Θ) in evidence

.…”

Section: Preliminariesmentioning

confidence: 99%

“…In addition, another feature of decision-level fusion is that the fusion information has a relatively small amount of calculation. So it can be flexibly used in multi-sensor classification problems with its strong capacity for heterogeneous sensors, such as fault diagnosis [ 3 ], target recognition [ 4 , 5 ], environment grade evaluation [ 6 ], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Wang, J [ 4 ] analyzed the existing methods and found that in the process of weight allocation of evidence, most scholars focus on the conflict measurement of evidence while ignoring or underestimating the influence of evidence fuzziness on fusion results. Therefore, the current weight calculation mainly depends on the degree of conflict.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

Yao

Dai

et al. 2023

Sensors

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A single sensor is prone to decline recognition accuracy in the face of a complex environment, while the existing multi-sensor evidence theory fusion methods do not comprehensively consider the impact of evidence conflict and fuzziness. In this paper, a new evidence weight combination and probability allocation method is proposed, which calculated the degree of evidence fuzziness through the maximum entropy principle, and also considered the impact of evidence conflict on fusing results. The two impact factors were combined to calculate the trusted discount and reallocate the probability function. Finally, Dempster’s combination rule was used to fuse every piece of evidence. On this basis, experiments were first conducted to prove that the existing weight combination methods produce results contrary to common sense when handling high-conflicting and high-clarity evidence, and then comparative experiments were conducted to prove the effectiveness of the proposed evidence weight combination and probability allocation method. Moreover, it was verified, on the PAMAP2 data set, that the proposed method can obtain higher fusing accuracy and more reliable fusing results in all kinds of behavior recognition. Compared with the traditional methods and the existing improved methods, the weight allocation method proposed in this paper dynamically adjusts the weight of fuzziness and conflict in the fusing process and improves the fusing accuracy by about 3.3% and 1.7% respectively which solved the limitations of the existing weight combination methods.

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where

represents the number of focal elements (the number of elements in the recognition framework Θ) in evidence

.…”

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

Yao

Dai

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, Xiao Xiao (2019) extended the classical Jensen-Shannon (JS) divergence to belief functions, but the method does not consider the relationship between focal elements. Wang et al Wang et al (2022). on the other hand, by using the W distance to indicate the degree of fuzzy evidence and combines the degree of conflict calculated by the ranking factor and the Jousselme distance to determine the weight of the evidence, is the latest baseline approach to fusion in this field and is the method used in this paper.…”

Section: D-s Evidence Theorymentioning

confidence: 99%

“…(2) Measure the centrality, transmissibility, and prestige indicators of each node in turn, and fuse the three types of indicators using the improved D-S evidence theory algorithm proposed by Wang et al Wang et al (2022). to calculate the probability of each node being important or unimportant;…”

Section: Experiments IImentioning

confidence: 99%

A Multiple Features Fusion based Node Importance Measure for Rumor Control in Social Networks

Wang

Huang

et al. 2023

Preprint

Self Cite

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In the field of social networks rumor control, finding the important nodes with the greatest propagating influence is of great practical importance to effectively control the propagation of rumors. In order to solve the problem that the network coupling information and information transfer mechanism in the existing node importance metric affects the accuracy of the metric, we propose a measure of node importance based on Multiple Feature Fusion (MFF). The method uses an improved Dempster-Shafer evidence theory to fuse the centrality, transmissibility, and prestige measurement of nodes and rank the importance of nodes based on the fusion results. The proposed method was first evaluated against similar methods on six real networks in terms of robustness and vulnerability, as well as in terms of SIR propagation characteristics. Then, we simulated the changes in the number of users who believed the rumor after the rumor was propagated in three cases: before the control, after suppress rumors by random nodes, and after suppress rumors by important nodes. The experimental results show that the proposed method is more accurate for node importance measurement and more effective in rumor control.

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Visualization of basic probability assignment

2022

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Applying geometry to the analysis and interpretation of basic probability assignment(BPA) is a unique research direction in evidence theory. Though the geometric representation of BPA has been proposed, the visualization method of BPA is still lack of sufficient research. In this paper, we propose a new BPA visualization method based on the vector representation of the BPA to illustrate the image of BPA directly. The basic point and the uncertain vectors can be obtained by the given BPA firstly, and then we connect these components to construct the image of BPA. Through the image of BPA, we can effectively analyze the interaction effect of focal elements in BPA, and observe the potential characteristics of BPA directly. Meanwhile, the geometric meanings of parameters in the vector representation of the BPA can be explained. Finally, the advantages and applications have been studied and discussed.

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An evidence combination rule based on a new weight assignment scheme

Cited by 16 publications

References 57 publications

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

A New Evidence Weight Combination and Probability Allocation Method in Multi-Sensor Data Fusion

A Multiple Features Fusion based Node Importance Measure for Rumor Control in Social Networks

Visualization of basic probability assignment

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