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An Optimal Probabilistic Transformation of Belief Functions Based on Artificial Bee Colony Algorithm

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Intelligent Computing Theory (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8588))

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Abstract

One of the key issues in the applications of Dempster-Shafer evidence theory is how to make a decision based on Basic Probability Assignment (BPA). Besides the widely known pignistic transformation of belief functions, another conventional method is Plausibility Transformation, which transforms BPA to probability distribution by normalizing plausibility function of every singleton proposition. However, these two methods are stuck with the problem of impreciseness. To overcome this drawback, a new transformation method based on Artificial Bee Colony Algorithm is proposed. The obtained probability has the maximum correlation coefficient with the original BPA. Numerical examples are used to illustrate the efficiency of the proposed method.

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Author information

Authors and Affiliations

  1. School of Air and Missile Defense, Air Force Engineering University, Xi’an, 710051, P.R. China

    Yafei Song, Xiaodan Wang, Lei Lei & Aijun Xue

Authors
  1. Yafei Song
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  2. Xiaodan Wang
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  3. Lei Lei
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  4. Aijun Xue
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Politecnico of Bari, Via Orabona, 4,, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongon, 2522, North Wollongon, NSW, Australia

    Prashan Premaratne

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© 2014 Springer International Publishing Switzerland

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Song, Y., Wang, X., Lei, L., Xue, A. (2014). An Optimal Probabilistic Transformation of Belief Functions Based on Artificial Bee Colony Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_11

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  • DOI: https://doi.org/10.1007/978-3-319-09333-8_11

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09332-1

  • Online ISBN: 978-3-319-09333-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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