Artificial Intelligence


An Improvement in Monotonicity of the Distance-Based Total Uncertainty Measure in Belief Function Theory

Authors: Xinyang Deng, Yong Deng

Measuring the uncertainty of evidences is an open issue in belief function theory. Recently, a distance-based total uncertainty measure for the belief function theory, indicated by ${TU}^I$, is presented. Some experiments show the efficiency of the ${TU}^I$ to measure uncertainty degree. In this paper, numerical example and theoretical analysis are illustrated that the monotonicity in ${TU}^I$ is not satisfied. To address this issue, an improved uncertainty measure ${TU}^I_E$ is proposed. The monotonicity for ${TU}^I_E$ is theoretically proved. Finally, through experimental comparison we show that ${TU}^I_E$ also has the desired high sensitivity to the evidence changes, which further indicates that the proposed ${TU}^I_E$ is better than ${TU}^I$.

Comments: 18 Pages.

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Submission history

[v1] 2016-05-07 23:23:50

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