A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

Sun, Lei; Chang, Yu-Ting; Pu, Jiexin; Yu, Hongbo; Yang, Zhe

doi:10.5028/jatm.v12.1173

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…The specific process of this kind of method is to preprocess the evidence using weight firstly, and then combine the processed evidence according to the Dempster combination rule. There are also two branches of such kind of methods, i. e., the weighted average correction method (Murphy, 2000;Sun et al, 2020;Chen et al, 2021) and the discount coefficient method (Jousselme et al, 2001;Lefèvre and Elouedi, 2013;Song et al, 2014;Hu et al, 2016;Xue et al, 2021;Xu and Deng, 2018). Compared with modifying the combination rule framework, such method retains the excellent mathematical properties of D-S evidence theory, and more effectively solve the problems caused by conflicting evidence and fuzzy evidence in combination, therefore it has better practical application value.…”

Section: Introductionmentioning

confidence: 99%

An Evidence Combination Rule Based on New Weight Assignment Scheme

wang

Wang

Huang

et al. 2022

Preprint

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Conflicting evidence and fuzzy evidence have a significant impact on the results of evidence combination in the application of evidence theory. However, the existing weight assignment methods can hardly reflect the significant influence of fuzzy evidence on the combination results. Therefore, a new method for assigning evidence weights and the corresponding combination rule are proposed. The proposed weight assignment method strengthens the consideration of fuzzy evidence and introduces the Wasserstein distance to compute the clarity degree of evidence which is an important reference index for weight assignment in the proposed combination rule and can weaken the effect of ambiguous evidence effectively. In the experiments, it's firstly verified that the impact of fuzzy evidence on the combination results is significant; therefore it should be fully considered in the weight assignment process. Then, the proposed combination rule with new weight assignment method is tested on a set of numerical arithmetic and Iris datasets. Compared with four existing methods, the results show that the proposed method has higher decision accuracy, F1 score, better computational convergence, and more reliable fusion results as well.

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Section: Introductionmentioning

confidence: 99%

An Evidence Combination Rule Based on New Weight Assignment Scheme

wang

Wang

Huang

et al. 2022

Preprint

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show abstract

“…Then the processed evidence is combined by Dempster rule. 24 The methods of preprocessing evidence can be divided into two categories: the weighted average correction method 25,26 and the discount coefficient method. 27,28 The key to these two processing methods is the determination of weight and discount coefficients.…”

Section: Introductionmentioning

confidence: 99%

A new correlation coefficient of BPA based on generalized information quality

Liang

Cai

2021

Int J of Intelligent Sys

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Dempster rule is commonly used to combine evidence from different sensors because of its excellent mathematical properties (commutativity and associativity). However, the conflict coefficient k of this rule cannot be reasonably and effectively express the conflict between evidence, which makes the Dempster rule greatly questioned. To describe in the relationship between evidence (conflict or correlation) more accurately, an evidence correlation coefficient based on generalized information quality is proposed. First of all, according to Deng entropy to measure the uncertainty of each evidence, and combined with the information quality of Yager, new generalized information quality is proposed, which performs well in measuring the basic probability assignment's certainty. Second, the evidence itself is modified by generalized information quality, and the evidence correlation coefficient is calculated based on the Pearson coefficient formula. A new measurement method of evidence conflict based on evidence correlation coefficient is proposed. Finally, combined with the evidence correlation coefficient and DEMATEL model, the evidence is discounted and combined. Numerous examples are used to analyze and compare the evidence conflict coefficient and the evidence combination results. The experimental results demonstrate that, compared with other evidence conflict measurement methods, the evidence conflict coefficient calculated by this method can reflect the difference between evidence more effectively. The result of evidence combination is more reasonable and accurate.

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An evidence combination rule based on a new weight assignment scheme

Wang

Huang

et al. 2022

Soft Comput

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A Weighted Evidence Combination Method Based on the Pignistic Probability Distance and Deng Entropy

Cited by 7 publications

References 34 publications

An Evidence Combination Rule Based on New Weight Assignment Scheme

An Evidence Combination Rule Based on New Weight Assignment Scheme

A new correlation coefficient of BPA based on generalized information quality

An evidence combination rule based on a new weight assignment scheme

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