Discountings of a Belief Function Using a Confusion Matrix

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 to correct raw data, based on learnt decisions given by the confusion matrix. These corrections differ according to the use of classical or contextual or distance using discountings. An illustrative example is presented to emphasize the interest and also to show the differences between these adjustments. We carry experimentations on real databases to analyze and interpret these adjustment approaches.