A novel BPA derivation method using linguistic fuzzy sets in dempster-shafer theory

Abstract

In this study, we present a new method for developing Basic Probability Assignment (BPA) using Linguistic Fuzzy Sets (LFS). Dempster-Shafer Theory (DST) is used to efficiently combine different data sources. The use of LFS provides a robust structure for managing uncertainty in decision making by representing knowledge in linguistic terms. However, effectively integrating multiple uncertain sources requires a strong probabilistic approach. To overcome this challenge, we propose a method to derive BPA values directly from linguistic terms, ensuring compatibility with the DST fusion rule. We then use the Dempster-Shafer combination rule to combine information from various sources, thereby improving the reliability of the decision-making process.