A Novel Optimization-Based Combination Rule for Dempster-Shafer Theory

In this study, a new methodology for combining probability masses from different sources is proposed for Dempster-Shafer theory. Unlike the existing works in the literature, this methodology treats the combination problem as an optimization problem and proposes an objective function that uses conflict and entropy measures to solve this problem. The proposed objective function aims to minimize the conflict and also to maximize the entropy of the combined probability masses. Thus, the difference between the combined probability masses and the masses coming from the sources is minimized while being cautious and avoiding a final certain decision. This new methodology is tested in the Matlab environment and compared with the existing methods.