In this paper, a proposition is made to learn the parameters of evidential contextual correction mechanisms from a learning set composed of soft labelled data, that is data where the true class of each object is only partially known. The method consists in optimizing a measure of discrepancy between the values of the corrected contour function and the ground truth also represented by a contour function. The advantages of this method are illustrated by tests on synthetic and real data.
In this paper, an improvement of the quality of an evidential source of information is proposed using contextual corrections depending on partial decisions obtained from an interval dominance relation on the source outputs. Numerical experiments with the EkNN classifier and synthetic and real data allows us to illustrate the performances and the interest of this method.
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