Optimizing Cardinality-aware Combination Rules in Belief Functions Theory: an Enhanced Framework
Faouzi Sebbak, Mustapha Réda Senouci · 2024
Belief Functions Theory (BFT), also known as Dempster-Shafer theory, has emerged as a powerful framework for uncertain modeling and reasoning in various domains. At the heart of BFT, lies the concept of combination rules, which govern the fusion process and have a profound impact on the quality and reliability of the results. Currently, there is a noticeable trend in the field, with a growing emphasis on cardinality-aware combination rules, reflecting the need for more nuanced and context-aware approaches to uncertainty management. Despite this, there exists a notable gap in the implementation of cardinality-aware rules within existing frameworks, limiting their applicability in complex decision-making scenarios. This paper addresses this gap by proposing an enhanced MATLAB framework that optimizes computational complexity, thereby enabling efficient implementation of both current and future cardinality-aware combination rules. By providing detailed insights into the framework’s structure, key functions, and examples of the implementation of these cardinality-aware rules, this paper aims to bridge the identified gap and enhance the usability of BFT in practical applications. The source code for this enhanced framework has been shared with the community.