Measuring total uncertainty in evidence theory

Deng, Zhenghong; Wang, Jianyu

doi:10.1002/int.22358

Cited by 18 publications

(16 citation statements)

References 40 publications

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“…It has the advantage of stability and reliability compared to other metrics. Deng et al [ 30 ] measured the uncertainty of the evidence itself by calculating the uncertainty interval distance of the evidence focal elements. However, finding the weight of the evidence based on uncertainty involves more steps and is more tedious than finding the weight based on certainty, so this paper proposes a method by which to combine the Hellinger distance of the evidence support interval and rejection interval to jointly measure the certainty of the evidence.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…For the latter, scholars have proposed various uncertainty measures based on information entropy, such as Yager’s [ 28 ] dissonance measure based on the likelihood function and Deng’s [ 29 ] evidence uncertainty measure based on Shannon entropy, but such methods deal with evidence in a one-sided manner, replacing the entire uncertainty interval with only part of the evidence information. Deng et al [ 30 ] developed a method for evaluating evidence uncertainty based on the Hellinger distance of the uncertainty interval, which is simple to compute and measures uncertainty well for a better integration effect. The relationship between evidence and the characteristics of the evidence itself do not affect each other and are both valid information available within the evidence, yet some current scholarly approaches to improving evidence theory consider only one of them to deal with the evidence, undermining the integrity of the evidentiary information.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory

Xiang

Huang

et al. 2022

Sensors

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The essential factors of information-aware systems are heterogeneous multi-sensory devices. Because of the ambiguity and contradicting nature of multi-sensor data, a data-fusion method based on the cloud model and improved evidence theory is proposed. To complete the conversion from quantitative to qualitative data, the cloud model is employed to construct the basic probability assignment (BPA) function of the evidence corresponding to each data source. To address the issue that traditional evidence theory produces results that do not correspond to the facts when fusing conflicting evidence, the three measures of the Jousselme distance, cosine similarity, and the Jaccard coefficient are combined to measure the similarity of the evidence. The Hellinger distance of the interval is used to calculate the credibility of the evidence. The similarity and credibility are combined to improve the evidence, and the fusion is performed according to Dempster’s rule to finally obtain the results. The numerical example results show that the proposed improved evidence theory method has better convergence and focus, and the confidence in the correct proposition is up to 100%. Applying the proposed multi-sensor data-fusion method to early indoor fire detection, the method improves the accuracy by 0.9–6.4% and reduces the false alarm rate by 0.7–10.2% compared with traditional and other improved evidence theories, proving its validity and feasibility, which provides a certain reference value for multi-sensor information fusion.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory

Xiang

Huang

et al. 2022

Sensors

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show abstract

“…In Dempster-Shafer evidence theory, how to measure the divergence and conflicts between the evidence remains an open issue 37 . There are many uncertainty measurement methods 38 , such as ambiguity measure 39 , total uncertainty measure 40 , divergence measure 41 , the correlation coefficient 42 , and the fractal-based belief entropy 43 . Recently, Xiao 44 proposed the belief divergence measure (BJS) on the basis of the Jensen-Shannon divergence measure 45 .…”

Section: Introductionmentioning

confidence: 99%

Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure

Liu

Tang

2022

Sci Rep

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Failure mode and effects analysis (FMEA) is an effective model that identifies the potential risk in the management process. In FMEA, the priority of the failure mode is determined by the risk priority number. There is enormous uncertainty and ambiguity in the traditional FMEA because of the divergence between expert assessments. To address the uncertainty of expert assessments, this work proposes an improved method based on the belief divergence measure. This method uses the belief divergence measure to calculate the average divergence of expert assessments, which is regarded as the reciprocal of the average support of assessments. Then convert the relative support among different experts into the relative weight of the experts. In this way, we will obtain a result with higher reliability. Finally, two practical cases are used to verify the feasibility and effectiveness of this method. The method can be used effectively in practical applications.

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“…Belief intervals for singletons, whose lower and upper bounds are, respectively, the minimum and maximum support of information represented by the BPA in the corresponding singleton, have recently received considerable attention for calculating the uncertainty-based information involved in a BPA. [19][20][21][22] In fact, they are easier to manage than the BPA to represent the uncertainty-based information, as explained in Reference [19]. Belief intervals have also been used in Reference [23] for quantifying the uncertainty-based information involved in a Dnumber, 24 a generalization of the concept of BPA in DST.…”

Section: Introductionmentioning

confidence: 99%

Required mathematical properties and behaviors of uncertainty measures on belief intervals

Moral-García

Abellán

2021

Int J Intell Syst

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The Dempster–Shafer theory of evidence (DST) has been widely used to handle uncertainty‐based information. It is based on the concept of basic probability assignment (BPA). Belief intervals are easier to manage than a BPA to represent uncertainty‐based information. For this reason, several uncertainty measures for DST recently proposed are based on belief intervals. In this study, we carry out a study about the crucial mathematical properties and behavioral requirements that must be verified by every uncertainty measure on belief intervals. We base on the study previously carried out for uncertainty measures on BPAs. Furthermore, we analyze which of these properties are satisfied by each one of the uncertainty measures on belief intervals proposed so far. Such a comparative analysis shows that, among these measures, the maximum of entropy on the belief intervals is the most suitable one to be employed in practical applications since it is the only one that satisfies all the required mathematical properties and behaviors.

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Measuring total uncertainty in evidence theory

Cited by 18 publications

References 40 publications

A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory

A Multi-Sensor Data-Fusion Method Based on Cloud Model and Improved Evidence Theory

Managing uncertainty of expert’s assessment in FMEA with the belief divergence measure

Required mathematical properties and behaviors of uncertainty measures on belief intervals

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