MMGET: A Markov model for generalized evidence theory
This work addresses the need for better uncertainty management in sequential, real-world data for researchers in evidence theory and related fields, but it appears incremental as it builds upon existing generalized evidence theory.
The authors tackled the problem of managing uncertain information in complex, open-world scenarios by introducing a Markov model into generalized evidence theory, which they claim helps extract complete information volume from evidence, as verified through numerical examples.
In real life, lots of information merges from time to time. To appropriately describe the actual situations, lots of theories have been proposed. Among them, Dempster-Shafer evidence theory is a very useful tool in managing uncertain information. To better adapt to complex situations of open world, a generalized evidence theory is designed. However, everything occurs in sequence and owns some underlying relationships with each other. In order to further embody the details of information and better conforms to situations of real world, a Markov model is introduced into the generalized evidence theory which helps extract complete information volume from evidence provided. Besides, some numerical examples is offered to verify the correctness and rationality of the proposed method.