Étude des algorithmes d'approximation de fonctions de croyance généralisées

La recherche présentée ici consiste à résoudre le problème de difficulté calculatoire de la fusion d’informations dans le cadre de la théorie de l’évidence de Dempster-Shafer, ainsi que celui de la théorie de Dezert-Smarandache. On présente des études sur l’utilisation d’une variété d’algorithmes d’approximation connus ainsi que sur un nouvel algorithme d’approximation. On présente aussi une étude sur les métriques connues de distance entre corps d’évidence ainsi que deux nouvelles métriques. Enfin, on montre une étude de la possibilité d’employer une méthode d’optimisation afin de sélectionner automatiquement les paramètres d’approximation à l’aide de critères de performance. Mots-clés : Dezert, Smarandache, Dempster, Shafer, Fusion, Fonctions de croyance.This research is about the solving of the computational difficulty of data fusion in the evidence theory of Dempster-Shafer theory and Dezert-Smarandache theory. We study the use of a variety of known approximation algorithms as well as a new approximation algorithm that we propose. We also study known metrics between bodies of evidence as well as two new metrics that we develop. Finally, we study the possibility of using an optimization method to automatically select the parameters of approximation with performance criteria. Keywords: Dezert, Smarandache, Dempster, Shafer, Fusion, Belief functions

Fusion of Information and Analytics: A Discussion on Potential Methods to Cope with Uncertainty in Complex Environments (Big Data and IoT)

International audienceInformation overload and complexity are core problems to most organizations of today. The advances in networking capabilities have created the conditions of complexity by enabling richer, real-time interactions between and among individuals, objects, systems and organizations. Fusion of Information and Analytics Technologies (FIAT) are key enablers for the design of current and future decision support systems to support prognosis, diagnosis, and prescriptive tasks in such complex environments. Hundreds of methods and technologies exist, and several books have been dedicated to either analytics or information fusion so far. However, very few have discussed the methodological aspects and the need of integrating frameworks for these techniques coming from multiple disciplines. This paper presents a discussion of potential integrating frameworks as well as the development of a computational model to evolve FIAT-based systems capable of meeting the challenges of complex environments such as in Big Data and Internet of Things (IoT)