A novel information fusion method based on Dempster-Shafer evidence theory for conflict resolution

Yang, Jianping; Huang, Hong‐Zhong; Miao, Qiang; Sun, Rui

doi:10.3233/ida-2010-0474

Cited by 37 publications

(13 citation statements)

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“…34 Yang et al proposed the paradox combination algorithm based on an absolute di®erence factor of two pieces of evidence and a relative di®erence factor of two pieces of evidence for a speci¯c hypothesis with the consideration of local attributions to local con°ict. 35 These studies show that the D-S evidence theory has been widely used and deeply studied. However, there are two main questions that remain to be answered:…”

Section: Introductionmentioning

confidence: 99%

Quantification Classification Algorithm of Multiple Sources of Evidence

Yang

Huang

Liu

et al. 2015

Int. J. Info. Tech. Dec. Mak.

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Although Dempster-Shafer (D-S) evidence theory and its reasoning mechanism can deal with imprecise and uncertain information by combining cumulative evidences for changing prior opinions of new evidences, there is a de¯ciency in applying classical D-S evidence theory combination rule when con°ict evidence appear À À À con°ict evidence causes counter-intuitive results. To address this issue, alternative combination rules have been proposed for resolving the appeared con°icts of evidence. An underlying assumption is that con°ict evidences exist, which, however, is not always true. Moreover, it has been veri¯ed that con°ict factors may not be accurate to characterize the degree of con°ict. Instead, the Jousselme distance has been regarded as a quanti¯cation criterion for the degree of con°ict because of its promising properties. To avoid the counter-intuitive results, multiple sources of evidence should be classi¯ed rst. This paper proposes a novel algorithm to quantify the classi¯cation of multiple sources of evidence based on a core vector method, and the algorithm is further veri¯ed by two examples. This study also explores the relationship between complementary information and con°icting evidence and discusses the stochastic interpretation of basic probability assignment functions.

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Section: Introductionmentioning

confidence: 99%

Quantification Classification Algorithm of Multiple Sources of Evidence

Yang

Huang

Liu

et al. 2015

Int. J. Info. Tech. Dec. Mak.

Self Cite

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“…In applying this theory, many challenges are increasingly being addressed. We can mention, conflict management through different sources of information [24], [25], Consideration of the relationship between various pieces of evidence before the fusion step [26], [27], the problem in body of evidence (BOE) generation [28], [29] and finally, the consideration of the frame of discernment (F OD) in uncertainty management [24], [30].…”

Section: Introductionmentioning

confidence: 99%

A New Uncertainty Measure in Belief Entropy Framework

Mambe¹,

́e²,

Georges³

et al. 2018

ijacsa

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Belief entropy, which represents the uncertainty measure between several pieces of evidence in the Dempster-Shafer framework, is attracting increasing interest in research. It has been used in many applications and is mainly based on the theory of evidence. To quantify uncertainty, several measures have been proposed in the literature. These measures, sometimes in extended or hybrid forms, use the Shannon entropy principle to determine uncertainty degree. However, the failure to consider the scale of the frame of discernment framework remains an open issue in quantifying uncertainty. In this paper, we propose a new uncertainty measure that takes into account the power set of the frame of discernment. After analysing the different existing methods, we show the performance and effectiveness of our proposed approach.

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“…Many scholars [4][5][6][7][8][9][10][11][12][13][14][15][16][17][19][20][21][22][23][26][27][28][29][30]34,36,40,41] focused on conflict analysis of information systems, and improved the relationship between the two sides of a conflict by finding the essence of the conflict issue. For example, Deja [5] examined nature of conflicts as we are formally defining the conflict situation model.…”

Section: Introductionmentioning

confidence: 99%

“…Skowron et al [29] explained the nature of conflict and defined the conflict situation model in a way to encapsulate the conflict components in a clear manner. Yang et al [34] investigated evidence conflict and belief convergence based on the analysis of the degree of coherence between two sources of evidence and illustrated the stochastic interpretation for basic probability assignment. Yu et al [36] provided the supporting probability distance to characterize the differences among bodies of evidence and gave a new combination rule for the combination of the conflicting evidence.…”

Section: Introductionmentioning

confidence: 99%

Conflict Analysis for Pythagorean Fuzzy Information Systems

et al. 2017

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Pythagorean fuzzy sets provide stronger ability than intuitionistic fuzzy sets to model uncertainty information and knowledge, but little effort has been paid to conflict analysis of Pythagorean fuzzy information systems. In this paper, we present three types of positive, central, and negative alliances with different thresholds, and employ examples to illustrate how to construct the positive, central, and negative alliances. Then we study conflict analysis of Pythagorean fuzzy information systems based on Bayesian minimum risk theory. Finally, we investigate group conflict analysis of Pythagorean fuzzy information systems based on Bayesian minimum risk theory.

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A novel information fusion method based on Dempster-Shafer evidence theory for conflict resolution

Cited by 37 publications

References 0 publications

Quantification Classification Algorithm of Multiple Sources of Evidence

Quantification Classification Algorithm of Multiple Sources of Evidence

A New Uncertainty Measure in Belief Entropy Framework

Conflict Analysis for Pythagorean Fuzzy Information Systems

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