Study of Distributed Data Fusion Using Dempster’s Rule and Cautious Operator

Guyard, Romain; Cherfaoui, Véronique

doi:10.1007/978-3-319-99383-6_13

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Idempotence is required by some combination rules, especially when the dependencies between sources are ill-known, as this property ensures that the same information supplied by two possibly dependent sources will remain unchanged after merging (combination) [41]. This property also guarantees that identical information items are not counted multiple times in the fusion process, a feature often required in cases where the fusion process is decentralized [42], which may well happen in clustering processes with a high number of evidence corpus.…”

Section: Idempotence In Combination Processmentioning

confidence: 99%

On Computing Evidential Centroid Through Conjunctive Combination: An Impossibility Theorem

Zhang

Destercke

Zhang

et al. 2023

IEEE Trans. Artif. Intell.

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The theory of belief functions (TBF) is now a widespread framework to deal and reason with uncertain and imprecise information, in particular to solve information fusion and clustering problems. Combination functions (rules) and distances are essential tools common to both the clustering and information fusion problems in the context of TBF, which have generated considerable literature. Distances and combination between evidence corpus of TBF are indeed often used within various clustering and classification algorithms, however their interplay and connections have seldom been investigated, which is the topic of this paper. More precisely, we focus on the problem of aggregating evidence corpus to obtain a representative one, and we show through an impossibility theorem that in this case, there is a fundamental contradiction between the use of conjunctive combination rules on the one hand, and the use of distances on the other hand. Rather than adding new methodologies, such results are instrumental in guiding the user among the many methodologies that already exist. To illustrate the interest of our results, we discuss different cases where they are at play. Impact Statement-Within the theory of belief functions, both distances and conjunctive combination rules can be used to achieve very similar purposes: evaluating the conflict between sources, performing supervised or unsupervised learning in presence of evidential information, or more simply obtaining a synthetic representation of multiple items of information. However, the results obtained by both approaches may show some inconsistency between them. This paper provides some insight as to why this may happen, showing that the two approaches are definitely at odds, and that using distances is, for instance, incompatible with some fundamental notions of the theory of belief functions, such as the least commitment principle. We illustrate the importance of the studied differences on problems such as k-centroid clustering, and discuss the importance of interpretations in such problems, which is rarely done in the literature.

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Section: Idempotence In Combination Processmentioning

confidence: 99%

On Computing Evidential Centroid Through Conjunctive Combination: An Impossibility Theorem

Zhang

Destercke

Zhang

et al. 2023

IEEE Trans. Artif. Intell.

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“…In [14] we proposed 4 different ways of data fusion, shown in figures 3, 4, 5 and 6 that are going to be tested in this scenario. The idea of these scenarios is to use Dempster's rule that increases the knowledge when data comes from two independent sources or the cautious operator when sources are dependent.…”

Section: Data Fusionmentioning

confidence: 99%

VANET distributed data fusion for traffic management

Guyard¹,

Cherfaoui²

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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In this article, we propose a distributed fusion algorithm to detect traffic congestion through the exchange of messages in vehicle network. This algorithm is based on the Dempster-Shafer theory that manages the uncertainties on data and sources of information. Each vehicle updates its database with local measurements (speed and interdistance) and information received from other vehicles and can calculate its route. Thanks to the collaboration, smart cars can avoid congested roads and take a better path to their destination. Several variants of the algorithm are studied and compared to a centralized approach through experiments carried out on the SUMO simulator using real urban road networks.

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Study of Distributed Data Fusion Using Dempster’s Rule and Cautious Operator

Cited by 2 publications

References 7 publications

On Computing Evidential Centroid Through Conjunctive Combination: An Impossibility Theorem

On Computing Evidential Centroid Through Conjunctive Combination: An Impossibility Theorem

VANET distributed data fusion for traffic management

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