Combining conflicting evidence using the DEMATEL method

Zhang, Weiquan; Deng, Yong

doi:10.1007/s00500-018-3455-8

Cited by 201 publications

(98 citation statements)

References 59 publications

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“…The Dempster-Shafer theory is an effective tool to express uncertainty information and make information fusion. [20][21][22][23] The research related to the Dempster-Shafer theory 1,2 has attracted much interest, including uncertainty measure, 24,25 evidential network, 26,27 evidence combination, [28][29][30] conflict management, 31,32 likelihood function, 33,34 and so on. [35][36][37] On the basis of the Dempster-Shafer theory, many new models handling uncertainty information have also been proposed, like D numbers.…”

Section: Dempster-shafer Evidence Theorymentioning

confidence: 99%

A new probability transformation method based on a correlation coefficient of belief functions

2019

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The Dempster‐Shafer evidence theory is widely used in many fields of information fusion because of its advantage in handling uncertain information. One of the key issues in this theory is how to make decision based on a basic probability assignment (BPA). Currently, a feasible scheme is transforming a BPA to a distribution of probabilities. However, little attention was paid to the correlation between BPA and probability distribution. In this paper, a novel method about the probability transformation based on a correlation coefficient of belief functions is proposed. The correlation coefficient is a new measurement, which can effectively measure the correlation between BPAs. The proposed method aims at maximizing the correlation coefficient between the given BPA and the transformed probability distribution. On the basis of this idea, the corresponding probability distribution can be obtained and could reflect the original information of the given BPA to the maximum extent. It is valid to consider that the proposed probability transformation method is reasonable and effective. Numerical examples are given to show the effectiveness of the proposed method.

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Section: Dempster-shafer Evidence Theorymentioning

confidence: 99%

A new probability transformation method based on a correlation coefficient of belief functions

2019

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“…To identify the target, we adopt the method proposed in. However, instead of using Jousselme's distance, the proposed association coefficient is used to measure the similarity between the bodies of evidence.…”

Section: Application In Target Recognition Systemmentioning

confidence: 99%

An association coefficient of a belief function and its application in a target recognition system

Pan

Deng

2019

Int J Intell Syst

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The conflict problem in D-S evidence theory has attracted the attention of many scholars. Conflict coefficients are proposed to describe conflicts between bodies of evidence. The association coefficient as the opposite of the conflict coefficient is also used to measure the conflict. The larger the association coefficient, the smaller the conflict degree, and the higher the similarity between the evidence bodies, and vice versa.In this paper, the degree of association is defined by Deng Entropy, and a new association coefficient is proposed based on the basic inequality. The nature of the new association coefficient and conflict coefficients is explored using examples. Finally, the association coefficient combined with the D-S combination rule is applied to the target recognition system, and accurate results are obtained.association coefficient, conflict coefficients, conflict problem, Deng entropy, D-S evidence theory, target recognition system | INTRODUCTIONThe D-S evidence theory was first proposed by Dempster 1 in 1967 and later developed by his student Shafer 2 in 1976 as an imprecise reasoning theory, which is widely promoted. [3][4][5][6] The theory was first applied to expert systems, 7-9 and it was gradually applied in the fields of information fusion, 10-12 intelligence analysis, 9,13-16 evidential reasoning, [17][18][19] and multiattribute decision analysis. [20][21][22][23][24] As an indeterminate reasoning method which satisfies the conditions weaker than Bayesian probability theory; it has the ability to directly express "uncertainty" 25 and "don't know." 26 Therefore, the D-S evidence theory is flexible and convenient for describing uncertainties. However, D-S evidence theory has produced counterintuitive results in the fusion of a highly conflicting example proposed by Zadeh. 27 Therefore, the issue of conflict between bodies of evidence has attracted more and more researchers so far. [28][29][30][31][32] Domestic and foreign scholars have proposed a large number of improved methods, which can be divided into two major categories: (a) modification of the combination rules of the classical D-S evidence theory to achieve the redistribution of conflicts in the case of evidence conflicts. [33][34][35][36] Such measures alleviate the conflict between the bodies of evidence to a certain extent, but at the same time, it is difficult for the new rules to maintain the original properties of the D-S combination rules. (b) Keep the classic combination rules unchanged, and preprocess conflict data before combination. [37][38][39][40][41][42] Haenni 43 believes that the modification of the data model is in engineering, mathematics and philosophically more reasonable. However, regardless of the type of method used to deal with conflicts between evidence, the conflict coefficient is an extremely important parameter. Therefore, the most critical step before choosing the right combination of evidence is the measure of evidence conflict.To measure the conflict between evidence bodies, many researchers have proposed...

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“…In which u θ ( ) i is utility of θ i . If the size of Ω is n, then the n − 1 term should be considered, as shown in Equation (9).…”

Section: Shafer Structuresmentioning

confidence: 99%

A new matrix game with payoffs of generalized Dempster‐Shafer structures

Zhou

Xiao

2019

Int J Intell Syst

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How to deal with matrix game under uncertainty is an open issue. Compared with the Dempster‐Shafer structure, the insignificant deviation caused by the subjectivity of the expert is effectively eliminated generalized Dempster‐Shafer structures. In this paper, a new matrix game with payoffs of generalized Dempster‐Shafer structures is presented. Generalized Dempster‐Shafer structures presents probability nonspecificity and inaccuracy as interval values. Then payoff is determined by linear programming. A zero‐sum matrix game is illustrated the efficiency of the proposed method.

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Combining conflicting evidence using the DEMATEL method

Cited by 201 publications

References 59 publications

A new probability transformation method based on a correlation coefficient of belief functions

A new probability transformation method based on a correlation coefficient of belief functions

An association coefficient of a belief function and its application in a target recognition system

A new matrix game with payoffs of generalized Dempster‐Shafer structures

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