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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Granular-ball computing (GBC) is an established efficient, robust, and scalable learning method.
    • The generation of granular balls (GBs) is a core component of GBC, typically relying on methods like k-means.

    Purpose of the Study:

    • To accelerate the GB generation process in GBC.
    • To develop a parameter-free and adaptive GB generation method.
    • To provide mathematical models for GB covering.

    Main Methods:

    • Replaced k-means with a division-based approach for faster GB generation.
    • Introduced a novel adaptive method for GB generation, addressing GB overlap and other factors.
    • Developed mathematical models for GB covering.

    Main Results:

    • The division-based method significantly improves GB generation efficiency while maintaining accuracy comparable to k-means.
    • The adaptive method achieves parameter-free and truly adaptive GB generation.
    • Experimental results on real datasets confirm the effectiveness of both proposed methods.

    Conclusions:

    • The proposed GB generation methods offer significant improvements in efficiency and adaptiveness for GBC.
    • These advancements maintain high accuracy, making GBC more practical for real-world applications.
    • The open-source release of the code facilitates further research and application of these GBC techniques.