Counter Deception in Belief Functions Using Shapley Value Methodology

Counter deception is one of the main content in data fusion. The existence of deceptive data may cause great hidden dangers to the generation of correct decisions. While among previous studies, whether evidence should aggregate is still virgin and may become a fascinating question. In this paper, a new counter deception model based on the Shapley value methodology is proposed, which provides a perspective for determining the weight of evidence. Then, we present that the distance of evidence is a kind of “marginal contribution” to the anomaly of the entire fusion system. Moreover, we also investigated the properties of the proposed method to judge whether there is deceptive data in the information fusion based on the cooperation benefits of all basic belief assignment (BBA) combinations. Several numerical examples and a classification application were used to illustrate the practicability and effectiveness of the proposed methodology.