MMGET: A Markov model for generalized evidence theory

TL;DR

This paper introduces a Markov model integrated with generalized evidence theory to better handle sequential and complex uncertain information in real-world scenarios, enhancing information extraction and management.

Contribution

It proposes a novel Markov model for generalized evidence theory, improving the representation and processing of sequential uncertain information in complex environments.

Findings

The method effectively extracts complete information from evidence.

Numerical examples verify the correctness and rationality of the approach.

The approach adapts well to complex, open-world situations.

Abstract

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.