Combining partially independent belief functions

Chebbah, Mouna; Martin, Arnaud; Yaghlane, Boutheina Ben

doi:10.1016/j.dss.2015.02.017

Cited by 16 publications

(7 citation statements)

References 28 publications

(59 reference statements)

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“…However, Dempster’s rule of combination has been discussed controversially almost right from the beginning. Criticism and research on alternatives compensating identified deficiencies is found in literature up to date [61,62,63,64]. The first discussion was raised by Zadeh and made public in 1984 [65], although the argumentation was already recorded in a technical report from 1979 [66]: Due to the normalisation applied in DRC, the combination result is counterintuitive in conflicting situations, in which experts are confident a certain proposition does not exist.…”

Section: Related Workmentioning

confidence: 99%

Information Fusion of Conflicting Input Data

Mönks

Dörksen

Lohweg

et al. 2016

Sensors

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Sensors, and also actuators or external sources such as databases, serve as data sources in order to realise condition monitoring of industrial applications or the acquisition of characteristic parameters like production speed or reject rate. Modern facilities create such a large amount of complex data that a machine operator is unable to comprehend and process the information contained in the data. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) in the input signals as well as conflicts between them. These aspects must be considered during information processing to obtain reliable results, which are in accordance with the real world. The analysis of the scientific state of the art shows that current solutions fulfil said requirements at most only partly. This article proposes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the μBalTLCS (fuzzified balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result. The performance of the contribution is shown by its evaluation in the scope of a machine condition monitoring application under laboratory conditions. Here, the MACRO system yields the best results compared to state-of-the-art fusion mechanisms. The utilised data is published and freely accessible.

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Section: Related Workmentioning

confidence: 99%

Information Fusion of Conflicting Input Data

Mönks

Dörksen

Lohweg

et al. 2016

Sensors

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“…Though D-S theory has advantages in decreasing indeterminacy by reserving the common information and fusing multi-source estimation without prior weights, it also has limitations. The open issues in D-S theory include conflict evidence fusion [46]- [49], dependent evidence fusion [50], [51], generation of mass function [52], belief entropy for evidence evaluation [53], [54], decision-making based on mass function [55], [56], incomplete information processing in D-S theory framework [57], approximation of mass function [58] and so on. In general, D-S theory is effective in evidence modeling and uncertain information fusion.…”

Section: Introductionmentioning

confidence: 99%

A Novel Failure Mode and Effects Analysis Model Using Triangular Distribution-Based Basic Probability Assignment in the Evidence Theory

Zheng

Tang

2020

IEEE Access

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Failure mode and effects analysis (FMEA) is a typical risk assessment and prevention technology, in which different experts provide different assessments on the target system to identify the risk grades of its components. Sometimes, the assessments contain conflict information. How to manage and fuse the conflict assessment information is an open issue. We propose a triangular distribution-based basic probability assignment (TDBPA) method to model and fuse the conflict risk level coming from different experts' assessments in the frame of Dempster-Shafer evidence theory. First, the subjective assessments of risk analysis from domain experts are modeled with belief structure in Dempster-Shafer evidence theory. Then, the assessments are transformed as the TDBPA function. Thirdly, the conflict risk assessments from the FMEA team for failure analysis can be fused with Dempster rule of combination. After that, the modified risk priority number (RPN) model based on fused assessment can be calculated for ranking of failure modes. Finally, recommended actions should be taken for prevention of potential risk items. We verify the rationality and efficiency of the proposed method with a case study in the blades of an aircraft turbine. In short, the presented FMEA methodology procedure in this paper is well organized so that we can apply it in a more simple and understandable way. Utilizing the character of triangular distribution, taking adjacent values into account, TDBPA method can smooth the conflict assessment for information fusion. In addition, the shortcoming of repeating values in classical RPN is eliminated in the proposed method, which improves the ability for risk assessment of FMEA. INDEX TERMS Failure mode and effects analysis (FMEA), Dempster-Shafer evidence theory, basic probability assignment, triangular distribution, failure analysis, knowledge reasoning.

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“…Data mining techniques typically incorporate clustering algorithms (Cheng et al, 2019, Zhu et al, 2019), density estimation techniques (Aggarwal, 2007) and support vector classification (Aggarwal, 2015; Xu et al, 2017). The theory of belief function that is well known as evidence theory is a general technique to express data uncertainty (Chebbah et al, 2015; Denoeux, 2013; Quost and Denoeux, 2016).…”

Section: Introductionmentioning

confidence: 99%

A novel mobile agent-based distributed evidential expectation maximization algorithm for uncertain sensor networks

Mozaffari

Safarinejadian

Shasadeghi

2020

Transactions of the Institute of Measurement and Control

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In this paper, a novel mobile agent-based distributed evidential expectation maximization (MADEEM) algorithm is presented for sensor networks. The proposed algorithm is used for probability density function estimation and data clustering in the presence of uncertainties in sensor measurements. It is assumed that the sensor measurements are statistically modeled by a common Gaussian mixture model. The proposed algorithm maximizes a new generalized likelihood criterion in an iterative and distributed manner. For this purpose, mobile agents compute some local sufficient statistics by using local measurements of each sensor node. After the local computations, the global sufficient statistics are updated. At the end of iterations, the parameters of the probability density function are updated by using the global sufficient statistics. The mentioned process will be continued until the convergence criterion is satisfied. Convergence analysis of the proposed algorithm is also presented in this paper. After the convergence analysis, the simulation results show the promising performance of the proposed algorithm. Finally, the last part of the paper is devoted to the concluding remarks.

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Combining partially independent belief functions

Cited by 16 publications

References 28 publications

Information Fusion of Conflicting Input Data

Information Fusion of Conflicting Input Data

A Novel Failure Mode and Effects Analysis Model Using Triangular Distribution-Based Basic Probability Assignment in the Evidence Theory

A novel mobile agent-based distributed evidential expectation maximization algorithm for uncertain sensor networks

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