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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)
The paper untitled " Belief functions combination and conflict management " has been published in a recent issue of the journal [1]. The problem of information combination is introduced in the context of Dempster-Shafer theory of evidence or belief function theory [2]. The authors adopt the Transferable Belief Model (TBM) point of view, a non-probabilistic(More)
In this article, the contextual discounting of a belief function, a classical discounting generalization, is extended and its particular link with the canoni-cal 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(More)
In this paper, we present an analysis of different approaches relative to the correction of belief functions based on the results given by a confusion matrix. Three different mechanisms based on discountings are detailed. These methods have the objective to assess the discounting rates to be assigned to a source of information. These discounting rates allow(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)