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    This study introduces a human-centered fuzzy associative classifier (FLeAC) framework. FLeAC integrates expert knowledge with data, enhancing diagnostic performance and creating more compact, understandable classifiers.

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    Area of Science:

    • Data Mining
    • Artificial Intelligence
    • Fuzzy Logic

    Background:

    • Fuzzy associative classifiers (FACs) are data-driven and do not leverage human expertise.
    • Human expert opinion is valuable for improving classifier performance, especially in subjective real-world problems.

    Purpose of the Study:

    • Introduce a human-centered framework (FLeAC) for FACs.
    • Integrate expert opinions and preferences with statistical data for enhanced classification.
    • Develop and evaluate an extended fuzzy CFAR (f-CFAR) algorithm within the FLeAC framework.

    Main Methods:

    • Developed the FLeAC framework using extended fuzzy logic and f-transformation.
    • Experts assign linguistic validity to classifier items, aggregated via collective intelligence.
    • Extended the CFAR algorithm to f-CFAR, implementing variations to test rule validity and f-transformation operators.

    Main Results:

    • The f-CFAR algorithm demonstrated superior diagnostic performance compared to the original CFAR.
    • f-CFAR outperformed other rule-based classifiers in terms of rule count, condition count, and execution time.
    • The proposed framework leads to more compact and comprehensible classifiers with comparable accuracy.

    Conclusions:

    • The FLeAC framework effectively incorporates human expertise into FACs.
    • f-CFAR offers improved efficiency and interpretability while maintaining high accuracy.
    • This human-centered approach advances the application of fuzzy logic in data mining for complex problems.