Belief Interval-Based Distance Measures in the Theory of Belief Functions
针对现有证据距离测度的不足，基于焦元信度区间的Wasserstein距离，提出了两种新的严格距离测度（欧氏和切比雪夫形式），并通过实例、仿真和应用验证其合理性与有效性。
In belief functions related fields, the distance measure is an important concept, which represents the degree of dissimilarity between bodies of evidence. Various distance measures of evidence have been proposed and widely used in diverse belief function related applications, especially in performance evaluation. Existing definitions of strict and nonstrict distance measures of evidence have their own pros and cons. In this paper, we propose two new strict distance measures of evidence (Euclidean and Chebyshev forms) between two basic belief assignments based on the Wasserstein distance between belief intervals of focal elements. Illustrative examples, simulations, applications, and related analyses are provided to show the rationality and efficiency of our proposed measures for distance of evidence.