A belief entropy-based approach for conflict resolution in IoT applications

Mambe, Moise Digrais; Oumtanaga, Souleymane; Anoh, Georges Nogbou

doi:10.1109/sccic.2018.8584552

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…https://engine.scichina.com/doi/10.1007/s11432-020-3006-9 [105][106][107][108] Risk assessment 3 6.38 [109][110][111] Multi-criteria decision-making 3 6.38 [26,112,113] Decision-making 2 4.26 [114,115] Emergency management 2 4.26 [116,117] Quantum decision 2 4.26 [118,119] Organizational management 1 2.13 [120] IoT applications 1 2.13 [121] Language model 1 2.13 [122] Medical diagnosis 1 2.13 [98] Besides, Ref. [77] also discussed the maximum entropy and the applications of proposed entropies.…”

Section: Definition 20 (Renyi-deng Entropy [77]) the R-d Entropy Is mentioning

confidence: 99%

“…Kang [120] constructed the stable hierarchy organization from the perspective of the maximum Deng entropy. A Deng entropy-based approach for conflict resolution in IoT applications was developed in [121]. And a few other applications, such as language model construction [122] and medical diagnosis [98] were also conducted by using Deng entropy.…”

Section: Applications In Other Fieldsmentioning

confidence: 99%

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Uncertainty measure in evidence theory

Deng

2020

Sci. China Inf. Sci.

285

108

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As an extension of probability theory, evidence theory is able to better handle unknown and imprecise information. Owing to its advantages, evidence theory has more flexibility and effectiveness for modeling and processing uncertain information. Uncertainty measure plays an essential role both in evidence theory and probability theory. In probability theory, Shannon entropy provides a novel perspective for measuring uncertainty. Various entropies exist for measuring the uncertainty of basic probability assignment (BPA) in evidence theory. However, from the standpoint of the requirements of uncertainty measurement and physics, these entropies are controversial. Therefore, the process for measuring BPA uncertainty currently remains an open issue in the literature. Firstly, this paper reviews the measures of uncertainty in evidence theory followed by an analysis of some related controversies. Secondly, we discuss the development of Deng entropy as an effective way to measure uncertainty, including introducing its definition, analyzing its properties, and comparing it to other measures. We also examine the concept of maximum Deng entropy, the pseudo-Pascal triangle of maximum Deng entropy, generalized belief entropy, and measures of divergence. In addition, we conduct an analysis of the application of Deng entropy and further examine the challenges for future studies on uncertainty measurement in evidence theory. Finally, a conclusion is provided to summarize this study.

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Section: Definition 20 (Renyi-deng Entropy [77]) the R-d Entropy Is mentioning

confidence: 99%

Section: Applications In Other Fieldsmentioning

confidence: 99%

Uncertainty measure in evidence theory

Deng

2020

Sci. China Inf. Sci.

285

108

View full text Add to dashboard Cite

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“…However, conflict management is still an open issue [36]- [38]. Even conflict measurement of BPA is argued [39]- [42].…”

Section: Introductionmentioning

confidence: 99%

Pignistic Belief Transform: A New Method of Conflict Measurement

2020

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To measure conflict between two basic probability assignment functions plays the key role of conflict management in Dempster-shafer evidence theory. In this paper, a new conflict measure is proposed. First, the classical pignistic probability transform (PPT) is generalized as pignistic belief transform (PBT). One of the advantages of PBT is that it can assign belief to multiple sets. When the belief is assigned to single element, the proposed PBT is degenerated as classical PPT. Then, the betting distance of two pignistic belief transforms is proposed, which can be used as a new conflict degree of BPAs. Finally, a numerical example is illustrated to show the use of the proposed method to combine conflicting evidence. INDEX TERMS Dempster-Shafer evidence theory, belief function, conflict, pignistic belief transform.

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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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A belief entropy-based approach for conflict resolution in IoT applications

Cited by 7 publications

References 19 publications

Uncertainty measure in evidence theory

Uncertainty measure in evidence theory

Pignistic Belief Transform: A New Method of Conflict Measurement

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

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