Weighted evidence combination based on distance of evidence and uncertainty measure

Han, Deqiang; Deng, Yong; Han, Chongzhao; Hou, Zhiqiang

doi:10.3724/sp.j.1010.2011.00396

Cited by 46 publications

(30 citation statements)

References 9 publications

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“…In our previous work, Deng et al [26] put forward an evidence combination based on weighted average of evidences. Han et al 's approach [27] which reposed Deng et al 's [26] includes two steps: firstly, generate the weights based on the distance of evidence; then, modify the weights generated in the first step by taking advantage of ambiguity measure (AM) [50]. We come up with a new modified weighted evidence combination because of the superior efficiency of Deng entropy [47,49] compared to AM [50] to measure uncertainty, which is confirmed through Example 4 of the comparison between AM and Deng entropy.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Thus, we could make use of Crd to directly replace in (14) in order to modify original BPAs. As mentioned in [27,52], the uncertainty degree can also be utilized to construct the weights. A preferable combination result has been gotten by using AM to modify Crd acquired based on the distance of evidence.…”

Section: Determining Weight With Evidence Distance the Distance Of Ementioning

confidence: 99%

“…There are some methods to measure the conflict or confusion in a belief function [30]. To preprocess data, such as Murphy's simple average in [23], Deng et al 's weighted average on the basis of distance of evidences in [26], and Han et al 's modified weighted average in [27], and to amend the combination rule, such as Smets' unnormalized combination rule [24], Dubois and Prade's disjunctive combination rule [25], and Yager's combination rule [3], are two main methods to fix up highly conflicting evidences combination efficiently. It should be pointed that there are some other combination rules to address the dependence evidence issue, which may be another alternative to handle conflicts [31][32][33][34].…”

Section: Basics Of Evidence Theory Letmentioning

confidence: 99%

“…The three alternatives mentioned above are all examined and they all proposed a general combination framework. The main work of preprocessing bodies of evidences (BOEs) includes Murphy's simple average in [23], Deng et al 's weighted average on the basis of distance of evidences in [26], and Han et al 's modified weighted average in [27]. In [23], a simple averaging approach of the primitive BOEs is proposed, and in that case all BOEs are seen equally important, which is unreasonable in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Deng et al [26] got a better combination result according to combining the weight average of the masses for − 1 times. The approach proposed by Han et al [27] is a novel weighted evidence combination approach based on the distance of evidence and ambiguity measure (AM) to modify Deng et al 's work [26].…”

Section: Introductionmentioning

confidence: 99%

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Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Wang

Xiao

Deng

et al. 2016

International Journal of Distributed Sensor Networks

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Conflict management in Dempster-Shafer theory (D-S theory) is a hot topic in information fusion. In this paper, a new weighted evidence combination on the basis of the distance between evidence and entropy function is presented. The proposed approach is identified as two procedures. First, the weight is determined based on the distance of evidence. Then, the obtained weight value in the first step is modified by making advantage of Deng entropy function. Our proposed method can efficiently cope with high conflicting evidences with better performance of convergence. A numerical example is provided to demonstrate that the proposed method is reasonable and efficient in the end.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: Determining Weight With Evidence Distance the Distance Of Ementioning

confidence: 99%

Section: Basics Of Evidence Theory Letmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Wang

Xiao

Deng

et al. 2016

International Journal of Distributed Sensor Networks

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Belief intervals aggregation

Sun¹,

Guan²,

Xiao³

et al. 2018

Int. J. Intell. Syst.

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The combination rule is the core of Dempster-Shafer theory (DST), and there is no uniform aggregation rule adapting to all conditions. The construction of such a ruleis still an open and hot topic. In this article, we focus on this point. We focus on the belief interval, which is made up of belief function and plausibility function, in the form of [belief function, plausibility function], instead of basic belief assignment, to represent the DST. We aim at exploring a belief interval combination rule as the combination rule of DST. To do this, we contrast the belief interval with the intuitionistic fuzzy sets and construct the belief interval combination rule based on the intuitionistic fuzzy weighted averaging (IFWA) operator, which opens the door for DST combination rules to the aggregation operator perspective. Further, we find that directly using the IFWA operator as the belief interval combination rule poses three problems: the "'one' veto problem," converge easily close to [1,1] and the belief function from the belief interval combination rule is not normalized. To solve these problems, we add a normalization process in the belief interval combination rule to modify it, which can address all these three problems well.We also set up a series of examples to illustrate the belief interval combination rule, including a multisensor fusion scenario to compare it with some of the existing rules, which shows its superiorities in better stability and lower operational load. K E Y W O R D S aggregation operator, belief functions, belief intervals, combination rule, plausibility function Int J Intell Syst. 2018;2425-2447.wileyonlinelibrary.com/journal/int

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Ignorance‐aware safety and reliability analysis: A heuristic approach

Yazdi¹

2019

Quality & Reliability Eng

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In the current study, we aim to solve a group decision‐making problem on the basis of expert judgment with consideration of both global and local ignorance in system safety assessment. When data are acquired from various sources, especially subjective ones, the estimation of the data's precise value is challenging. In such a case, the basic belief assignment (bba) function is utilized to cope with the decision‐making procedure on the basis of Dempster‐Shafer theory (DST). DST, as an effective tool for manipulating inaccurate values, stills is not adequate to obtain the optimum values under the consideration of global and local subjective ignorance. To address the aforementioned subjects in this study, an innovative heuristic approach using a multiobjective programming model is introduced to derive the optimal values (bba) in the system safety assessment. An illustrative example is provided in detail to show the applicability and feasibility of the introduced heuristic approach.

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Weighted evidence combination based on distance of evidence and uncertainty measure

Cited by 46 publications

References 9 publications

Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Weighted Evidence Combination Based on Distance of Evidence and Entropy Function

Belief intervals aggregation

Ignorance‐aware safety and reliability analysis: A heuristic approach

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