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    Area of Science:

    • Uncertainty Quantification
    • Evidence Theory
    • Information Fusion

    Background:

    • Dempster-Shafer evidence theory effectively models uncertain information but struggles with highly conflicting data.
    • The Dempster combination rule can yield counter-intuitive results, reducing decision-making accuracy.
    • Measuring conflict is crucial for enhancing decision levels in uncertain information processing.

    Purpose of the Study:

    • To propose a novel method for measuring discrepancies between bodies of evidence.
    • To introduce a new higher-order fractal belief Rényi divergence (HOFBReD) for quantifying uncertainty.
    • To develop an improved multisource information fusion algorithm based on the proposed discrepancy measure.

    Main Methods:

    • A dynamic fractal probability transformation model was developed to extract more information from basic belief assignments (BBAs).
    • Higher-order fractal belief Rényi divergence (HOFBReD) was proposed to measure discrepancies between BBAs, incorporating dynamic fractal probability transformation.
    • A novel multisource information fusion algorithm was designed utilizing the HOFBReD measure.

    Main Results:

    • HOFBReD effectively measures discrepancies between BBAs and possesses desirable properties related to probability transformation and divergence.
    • When dynamic fractal probability transformation concludes, HOFBReD aligns with Rényi divergence on pignistic probability transformations.
    • The proposed fusion algorithm demonstrated superior average pattern recognition accuracy across real-world datasets compared to existing methods.

    Conclusions:

    • The novel discrepancy measurement method and multisource information fusion algorithm contribute to improving decision levels when processing uncertain information.
    • HOFBReD offers a robust way to measure evidence discrepancy, especially in scenarios with conflicting data.
    • The findings suggest significant advancements in handling and fusing uncertain information for better decision-making.