A Basic Probability Assignment Generation Method Based on Normal Cloud Similarity and Its Application in Evidence Combination
Nuo Cheng, Xin Wang · International Journal of Intelligent Systems · 2025
The effective utilization of Dempster–Shafer (D‐S) evidence theory depends on the accurate establishment of the basic probability assignment (BPA). How to generate more effective BPA for different situations is always an open and hot topic. In this study, we present an approach for obtaining BPA based on the normal cloud model called combined fuzzy similarity measure (CFSM). The method first constructs the normal cloud model of each class of sample in each attribute by an interval number and uses the mean standard deviation to obtain the interval number for the test sample, thereby obtaining the normal cloud model. Then, the similarity between the test samples and the training samples is quantified based on the area relationship, thereby obtaining the BPA of the test samples. Finally, the evidence combination method based on the intuitionistic fuzzy earth mover’s distance (IFEMD) is used for experimental analysis. The experimental results verify the effectiveness of the method and its applicability in the case of small sample data.