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S2FS: Spatially-Aware Separability-Driven Feature Selection in Fuzzy Decision Systems.

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    Summary
    This summary is machine-generated.

    A new feature selection method, spatially-aware separability-driven feature selection (S²FS), improves fuzzy decision systems (FDSs). S²FS enhances classification and clustering performance by considering spatial data distribution for clearer decision boundaries.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Data Mining

    Background:

    • Feature selection is vital for fuzzy decision systems (FDSs), enhancing performance and interpretability.
    • Existing methods often neglect spatial data distribution and rely on limited distance metrics, hindering decision boundary clarity.
    • The spatial arrangement of data points significantly influences class separability and decision boundary definition.

    Purpose of the Study:

    • To introduce a novel feature selection framework, spatially-aware separability-driven feature selection (S²FS), for fuzzy decision systems.
    • To develop a spatially-aware separability criterion that integrates within-class compactness and between-class separation using spatial directional information.
    • To improve the predictive performance and interpretability of FDSs by selecting more discriminative features.

    Main Methods:

    • Proposing the spatially-aware separability-driven feature selection (S²FS) framework for FDSs.
    • Developing a novel criterion that combines scalar distances with spatial directional information for enhanced class structure characterization.
    • Employing a forward greedy strategy for iterative selection of the most discriminative features.

    Main Results:

    • S²FS consistently outperformed ten state-of-the-art feature selection algorithms across 11 real-world datasets.
    • The proposed method demonstrated superior performance in both classification accuracy and clustering.
    • Feature visualizations confirmed the enhanced interpretability of features selected by S²FS.

    Conclusions:

    • S²FS offers a significant advancement in feature selection for FDSs by incorporating spatial awareness.
    • The spatially-aware separability criterion effectively captures complex class structures, leading to improved model performance.
    • S²FS provides a more interpretable and accurate approach to feature selection in fuzzy decision systems.