Intuitionistic Fuzzy Information Fusion Optimized Through Entropy Reduction and Information Loss Measures Integrated with Diffusion Theory

Aggregation operators (AOs) are the most commonly used tools for the intuitionistic fuzzy (IF) information fusion, research into their construction methods and associated mathematical properties has already yielded abundant results. Indeed, the quality of information fusion is of paramount importance during the fusion process, as it directly affects the efficacy of the fusion task. However, the issue has drawn virtually no attention. Intuitively, an effective fusion tool should significantly reduce the uncertainty of fuzzy information and minimize information loss as much as possible. Moreover, information diffusion theory can, by means of a properly chosen diffusion function, produce a more accurate estimate of the relationships among fuzzy observations generated from an incomplete population than conventional statistical approaches. Inspired by these ideas to address this gap, we investigate measures for the quality of IF information fusion that integrate diffusion theory, and then explore a novel fusion method based on optimizing fusion quality. Empirical analysis demonstrates that our proposed approach significantly outperforms traditional AOs in enhancing the quality of information fusion.