Base belief function: an efficient method of conflict management

Dempster–Shafer evidence theory is widely used in many applications such as decision making and pattern recognition. However, Dempster’s combination rule often produces results that do not reflect the actual distribution of belief when collected evidence highly conflicts each other. In this paper, a base belief function is proposed to modify the classical basic probability assignment before combination in closed-world. Base belief function focuses on making combination result intuitive especially when evidences highly conflict each other. Compared to other methods, the combination result produced by proposed method is logical and consistent with real world with less computational complexity and better performance. The advantage of base belief function is that it can avoid high conflicts between evidences and is especially suitable for the situation where the evidences appear in sequence. Several numerical examples as well as experiments using real data sets from the UCI machine learning repository for classification are employed to verify the rationality of the proposed method.