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Calcul exact de faible complexité des décompositions conjonctive et disjonctive pour la fusion d'information.

Résumé : Dempster-Shafer Theory (DST) generalizes Bayesian probability theory, offering useful additional information, but suffers from a high computational burden. A lot of work has been done to reduce the complexity of computations used in information fusion with Dempster’s rule. Yet, few research had been conducted to reduce the complexity of computations for the conjunctive and disjunctive decompositions of evidence, which are at the core of other important methods of information fusion. In this paper, we propose a method designed to exploit the actual evidence (information) contained in these decompositions in order to compute them. It is based on a new notion that we call focal point, derived from the notion of focal set. With it, we are able to reduce these computations up to a linear complexity in the number of focal sets in some cases. In a broader perspective, our formulae have the potential to be tractable when the size of the frame of discernment exceeds a dozen possible states, contrary to the existing litterature. In addition, we have developed a complete open-source C++ framework containing an implementation of this method.
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Contributor : Franck Davoine <>
Submitted on : Monday, October 21, 2019 - 3:29:03 PM
Last modification on : Sunday, November 22, 2020 - 2:55:59 PM
Long-term archiving on: : Wednesday, January 22, 2020 - 4:15:53 PM


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  • HAL Id : hal-02322022, version 1


Maxime Chaveroche, Franck Davoine, Véronique Cherfaoui. Calcul exact de faible complexité des décompositions conjonctive et disjonctive pour la fusion d'information.. XXVIIème Colloque francophonede traitement du signal et des images (GRETSI 2019), Aug 2019, Lille, France. ⟨hal-02322022⟩



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