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Related Experiment Video

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CED: A Distance for Complex Mass Functions.

Fuyuan Xiao

    IEEE Transactions on Neural Networks and Learning Systems
    |April 21, 2020
    PubMed
    Summary

    A new Complex Evidential Distance (CED) measure is introduced for complex basic belief assignments (CBBAs) in evidence theory. This generalized distance metric enhances the ability to measure dissimilarity between pieces of evidence in complex spaces.

    Area of Science:

    • Uncertainty Quantification
    • Mathematical Theory

    Background:

    • Evidence theory effectively models uncertainty using basic belief assignments (BBAs).
    • Existing distance measures are limited to real-valued BBAs, not accommodating complex BBAs (CBBAs).

    Purpose of the Study:

    • To propose a generalized evidential distance measure for CBBAs in complex evidence theory.
    • To introduce the Complex Evidential Distance (CED) for measuring dissimilarity between CBBAs.

    Main Methods:

    • Development of the Complex Evidential Distance (CED) metric.
    • Verification of CED's properties as a strict distance metric (nonnegativity, nondegeneracy, symmetry, triangle inequality).
    • Demonstration of CED's generalization of traditional evidential distance (Jousselme et al.'s distance).

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    Main Results:

    • The CED is the first distance measure designed for CBBAs.
    • CED exhibits superior ability in measuring differences between pieces of evidence in complex spaces.
    • When CBBAs simplify to classical BBAs, CED converges to Jousselme et al.'s distance.

    Conclusions:

    • The proposed CED offers a more general framework for measuring evidential differences.
    • A CED-based decision-making algorithm is effective for pattern recognition, demonstrated in a medical diagnosis application.