Eric Lefevre

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Within the framework of evidence theory, data fusion consists in obtaining a single belief function by the combination of several belief functions resulting from distinct information sources. The most popular rule of combination, called Dempster’s rule of combination (or the orthogonal sum), has several interesting mathematical properties such as(More)
PSI, EA 2120, Université & INSA de Rouen Place Emile Blondel, F-76131 Mont Saint Aignan Cedex Eric.Lefevre@insa-rouen.fr IRCOM-SIC, UMR CNRS 6615, Bat. SP2MI BP 30179, Bd Marie et Pierre Curie, F-86962 Chasseneuil du Poitou Cedex colot@sic.sp2mi.univ-poitiers.fr HEUDIASYC, UMR 6599 CNRS, Université de Technologie de Compiègne BP 20529, F-60205 Compiègne(More)
In this article, the contextual discounting of a belief function, a classical discounting generalization, is extended and its particular link with the canonical disjunctive decomposition is highlighted. A general family of correction mechanisms allowing one to weaken the information provided by a source is then introduced, as well as the dual of this family(More)
The Analytic Hierarchy Process (AHP) method was introduced to help the decision maker to express judgments on alternatives over a number of criteria. In this paper, our proposal extends the AHP method to an uncertain environment, where the uncertainty is represented through the Transferable Belief Model (TBM), one interpretation of the belief function(More)
Several rules were proposed in the context of evidence combination to deal with the conflict generated between the combined information sources. However, in the belief function framework, as far as we know only one rule exists for managing dependent bodies of evidence which is the cautious rule. Unfortunately, this rule does not give the conflict its(More)