40 years of Dempster–Shafer theory

Denźux, Thierry

doi:10.1016/j.ijar.2016.07.010

Cited by 75 publications

(41 citation statements)

References 176 publications

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“…The idea of using completely monotone capacities, or belief functions, to model uncertainty dates back to Dempster's seminal work on statistical inference [6,7,8]. It was later elaborated by Shafer in his 1976 book [31,35], to become a full-fledged theory of uncertainty, now commonly referred to as Dempster-Shafer (DS) theory, evidence theory, or theory of belief functions [12]. In short, DS theory starts with the definition of a frame of discernment Ω containing all the possible values some variable X can take.…”

Section: Introductionmentioning

confidence: 99%

Decision-making with belief functions: A review

Denœux

2019

International Journal of Approximate Reasoning

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141

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Approaches to decision-making under uncertainty in the belief function framework are reviewed. Most methods are shown to blend criteria for decision under ignorance with the maximum expected utility principle of Bayesian decision theory. A distinction is made between methods that construct a complete preference relation among acts, and those that allow incomparability of some acts due to lack of information. Methods developed in the imprecise probability framework are applicable in the Dempster-Shafer context and are also reviewed. Shafer's constructive decision theory, which substitutes the notion of goal for that of utility, is described and contrasted with other approaches. The paper ends by pointing out the need to carry out deeper investigation of fundamental issues related to decision-making with belief functions and to assess the descriptive, normative and prescriptive values of the different approaches.

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Section: Introductionmentioning

confidence: 99%

Decision-making with belief functions: A review

Denœux

2019

International Journal of Approximate Reasoning

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141

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“…Since Zadeh presented the fusing paradoxes [30], research about conflict management has become one of the mainstream trend in decades [31]. Through the summarizing of existing references, recent studies can be classified into two mainstreams: methods based on the correction of original evidence sources and methods based on the modification of the DS combination rule [22].…”

Section: Sources Of Conflictsmentioning

confidence: 99%

“…Then, based on the weighted sum processing in (31), Q H fleetly calculates the rough mass assignment of fusing results. Accordingly, Q H can be used as a weighted value in (30) to reasonably allocate conflicting mass K.…”

Section: Conflict Redistributionmentioning

confidence: 99%

A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

Chen

Tian

2018

Journal of Sensors

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To eliminate potential evidence conflicts, an effective and accurate DS combination method is addressed in this paper. DS evidence theory is an outstanding information fusion approach with valid uncertainty treatment. Nevertheless, there are some limitations of the usage of the DS evidence theory. On the one hand, due to the complexity of a combat measurement environment and the inconsistency of sensor capabilities, sensor sources have enormous uncertainty, which would inevitably cause conflicts for evidence combination. On the other hand, DS combination rule realizes the unity property of fusing results with a compulsive normalization, which unavoidably leads to conflicting situations. To solve the possible evidence conflicts in a multisensor fusion system, we raise a robust DS combination method based on evidence correction and conflict redistribution. Firstly, two corrected indexes-the reliability index and consistency index-are separately addressed with the introduction of the Matusita distance function and closeness degree function. After the evidence modification based on two correction indexes, the conflicts caused by unreliable sensor sources are solved. Then, based on the corrected evidences, we put forward a weighted assignment of conflicting mass where the weight index lies on the evidence credibility. As the normalization step is abolished, the conflict redistribution strategy avoids the conflicts caused by straightforward normalization. Through comprehensive conflict management, the proposed DS combination method can not only guarantee the rationality and availability of fusing results, but also enhance the reliability and robustness of a multisensor system. Finally, three combination experiments with different conflicting degrees illustrate the advantage and superiority of the novel combination method for conflict management. Consequently, the innovation of the novel algorithm is verified.

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“…From the seminal work of Dempster and Shafer [18], the Transferable Belief Model proposed by Smets [19] makes the theory very popular and numerous applications have been carried out in very various domains [6], including (video-)surveillance problems such as target recognition [17] or multi-sensor system [2]. Combination of detectors has been proposed by [25] but in a very different context: multi-class problem but with much higher target resolution, whereas in this work we focus on spatial features of the considered descriptors.…”

Section: Related Workmentioning

confidence: 99%

An evidential framework for pedestrian detection in high-density crowds

Vandoni

Aldea

Hégarat‐Mascle

2017

2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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This paper addresses the problem of pedestrian detection in high-density crowd images, characterized by strong homogeneity and clutter. We propose an evidential fusion algorithm which is able to exploit multiple detectors based on different gradient, texture and orientation descriptors. The evidential framework allows us to model the spatial imprecision arising from each of the detectors. A first result of our study is that the fusion results underline clearly the good complementarity among the four descriptors we considered for this specific context. Moreover, the proposed algorithm outperforms a fusion solution based on Multiple Kernel Learning on difficult high-density crowd images acquired at Makkah at the height of the Muslim pilgrimage.

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40 years of Dempster–Shafer theory

Cited by 75 publications

References 176 publications

Decision-making with belief functions: A review

Decision-making with belief functions: A review

A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

An evidential framework for pedestrian detection in high-density crowds

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