The maximum entropy negation of basic probability assignment

In the field of information processing, negation is crucial for gathering information. Yager’s negative model of probability distribution has the property to reach maximum entropy allocation. However, how to reasonably model the negation operation of mass function in evidence theory is still an open issue. Therefore, a new negation operation based on the maximum Deng entropy of mass function is presented in this paper. After iterative negations, the focal elements are finally converging to a specific proportion, and the maximum Deng entropy is obtained. Then the characteristics of the new negation are explained through some numerical examples. Compared with existing negation models, the proposed model has maximal uncertainty. The convergence speed is affected by the scale of the frame of discernment. Finally, the rate of Deng entropy increases and some properties are discussed.