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 computed from the discounted beliefs and the actual values of data. Next, we develop a method for assessing the reliability of several sensors that are supposed to work jointly and their readings are aggregated. The discounting factors are computed on the basis of minimizing the distance between the pignistic probabilities computed from the combined discounted belief functions and the actual values of data.
In the belief function framework, a unique function is induced from the use of a combination rule so allowing to synthesize all the knowledge of the initial belief functions. When information sources are reliable and independent, the conjunctive rule of combination, proposed by Smets, may be used. This rule is equivalent to the Dempster rule without the normalization process. The conjunctive combination provides interesting properties, as the commutativity and the associativity. However, it is characterized by having the empty set, called also the conflict, as an absorbing element. So, when we apply a significant number of conjunctive combinations, the mass assigned to the conflict tends to 1 which makes impossible returning the distinction between the problem arisen during the fusion and the effect due to the absorption power of the empty set.The objective of this paper is then to define a formalism preserving the initial role of the conflict as an alarm signal announcing that there is a kind of disagreement between sources. More exactly, that allows to preserve some conflict, after the fusion by keeping only the part of conflict reflecting the opposition between the belief functions. This approach is based on dissimilarity measures and on a normalization process between belief functions. Our proposed formalism is tested and compared with the conjunctive rule of combination on synthetic belief functions.
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