Khaled Mellouli

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In the companion paper (Ben Yaghlane, Smets, & Mellouli, 2000a), we have enhanced the distinction between non-interactivity and doxastic independence in the context of the transferable belief model. The first corresponds to decompositionality of the belief function, whereas the second is defined as irrelevance preserved under Dempster’s rule of combination.(More)
This paper presents a method for assessing the reliability of a sensor in a classification problem based on the transferable belief model. First, we develop a method for the evaluation of the reliability of a sensor when considered alone. The method is based on finding the discounting factor minimizing the distance between the pignistic probabilities(More)
In this paper, we study the notion of marginal independence between two sets of variables when uncertainty is expressed by belief functions as understood in the context of the transferable belief model. We define the concepts of non-interactivity and irrelevance, that are not equivalent. Doxastic independence for belief functions is defined as irrelevance(More)
This paper extends the decision tree technique to an uncertain environment where the uncertainty is represented by belief functions as interpreted in the Transferable Belief Model (TBM). This so-called belief decision tree is a new classification method adapted to uncertain data. We will be concerned with the construction of the belief decision tree from a(More)
This paper addresses the issue of measuring similarity between pieces of uncertain information in the framework of possibility theory. In a first part, natural properties of such functions are proposed and a survey of the few existing measures is presented. Then, a new measure so-called Information Affinity is proposed to overcome the limits of the existing(More)
Naive Bayesian Classifiers, which rely on independence hypotheses, together with a normality assumption to estimate densities for numerical data, are known for their simplicity and their effectiveness. However, estimating densities, even under the normality assumption, may be problematic in case of poor data. In such a situation, possibility distributions(More)