A New Approach to Generate Basic Probability Assignment in the Open World
Pingting Li · 2025
Generalized evidence theory is an effective mathematical framework for addressing multi source information fusion problems. It is particularly adept at handling uncertainty, even in open-world scenarios. A crucial component of GET is the generalized basic probability assignment (BPA), which is used to quantify the belief assigned to different hypotheses. However, determining an accurate BPA remains a challenging and open problem, because it influences the result of evidence fusion. In this paper, we propose a novel method for generating BPA based on Gaussian fuzzy number (GFN). First, we construct a multi-subset representation model using GFN. Next, BPA are generated using this model along with specific strategies to ensure accuracy. Finally, the effectiveness is proved through experiments on the Iris dataset.