Abstract
Due to the nature of the Dempster combination rule, it may produce results contrary to intuition. Therefore, an improved method for conflict evidence fusion is proposed. In this paper, the belief entropy in D–S theory is used to measure the uncertainty in each evidence. First, the initial belief degree is constructed by using an improved base belief function. Then, the information volume of each evidence group is obtained through calculating the belief entropy which can modify the belief degree to get the final evidence that is more reasonable. Using the Dempster combination rule can get the final result after evidence modification, which is helpful to solve the conflict data fusion problems. The rationality and validity of the proposed method are verified by numerical examples and applications of the proposed method in a classification data set.
Highlights
Dempster-Shafer theory (D–S theory) [1,2] plays a vital role for addressing uncertainty in medical diagnosis [3], target recognition [4,5], fault diagnosis [6], classification [7,8,9], clustering [10,11,12], risk analysis [13] and many other fields [14]
In [16,17], Zadeh points out that if a conflict exists between the subjects of evidence, classical evidence theory will often get the opposite results in its normalization process
After evidence modification with the improved base belief function method, the information volume of E1 and E2 can be measured by belief entropy as: Iv( E1) = 2.8596, Iv( E2) = 2.8596, and the final evidence functions are m( a) = 0.4167, m(b) = 0.1667, m( a, b) = 0.3472
Summary
Dempster-Shafer theory (D–S theory) [1,2] plays a vital role for addressing uncertainty in medical diagnosis [3], target recognition [4,5], fault diagnosis [6], classification [7,8,9], clustering [10,11,12], risk analysis [13] and many other fields [14]. D–S theory can clearly measure the uncertainty of events, and provide the basis for decision-making by the data fusion results. In [16,17], Zadeh points out that if a conflict exists between the subjects of evidence, classical evidence theory will often get the opposite results in its normalization process.
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