Artificial Intelligence

   

Evidential Distance Measure in Complex Belief Function Theory

Authors: Fuyuan Xiao

In this paper, an evidential distance measure is proposed which can measure the difference or dissimilarity between complex basic belief assignments (CBBAs), in which the CBBAs are composed of complex numbers. When the CBBAs are degenerated from complex numbers to real numbers, the proposed distance will degrade into the Jousselme et al.’s distance. Therefore, the proposed distance provides a promising way to measure the differences between evidences in a more general framework of complex plane space.

Comments: 2 Pages.

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Submission history

[v1] 2019-06-24 05:00:48

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