English

Evidential distance measure in complex belief function theory

Artificial Intelligence 2019-07-02 v1

Abstract

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, i.e., BBAs, 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.

Keywords

Cite

@article{arxiv.1907.00716,
  title  = {Evidential distance measure in complex belief function theory},
  author = {Fuyuan Xiao},
  journal= {arXiv preprint arXiv:1907.00716},
  year   = {2019}
}

Comments

4 pages, 2 figures. arXiv admin note: text overlap with arXiv:1906.11409

R2 v1 2026-06-23T10:08:34.777Z