Gaussian convex evidence theory for ordered and fuzzy evidence fusion

Abstract

Convex evidence theory is the only way to handle ordered and fuzzy evidence fusion, however, conventional convex evidence theory has some drawbacks that make the fusion results are unreasonable in some cases, and not efficient in the scenario of massive data. To overcome above issues, in this article we proposed a novel convex evidence theory based on Gaussian function, we modified Gaussian function and use it to combine mass function of ordered propositions, we designed the formula of the parameters of Gaussian function, and proposed a more accurate method to find the most likely true proposition. We also proved the effectiveness of the proposed method. Theoretical analysis and experimental results demonstrate that the proposed method has lower time complexity and higher accuracy than state-of-the-art method.