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    This study introduces an efficient graph theory-based feature selection method for interval-valued information systems (IVISs). The approach effectively reduces model complexity and enhances performance for large datasets.

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    Area of Science:

    • Data Science
    • Machine Learning
    • Information Systems

    Background:

    • Feature selection is crucial in big data analytics.
    • Interval-valued data offers advantages over single-valued data for uncertain information.
    • Unsupervised attribute reduction for interval-valued information systems (IVISs) is underexplored, especially concerning computational efficiency for large datasets.

    Purpose of the Study:

    • To propose an effective and efficient feature selection method for IVISs.
    • To address the challenge of increased time complexity in feature selection for large interval-valued datasets.
    • To reduce model complexity in IVISs.

    Main Methods:

    • A novel feature selection method for IVISs based on graph theory.
    • Optimization of calculations using matrix power series properties.
    • A two-step approach: feature ranking (relevance and non-redundancy) and top-ranked attribute selection.

    Main Results:

    • Experimental validation on 14 public datasets against seven comparative algorithms.
    • Demonstrated effectiveness and efficiency of the proposed algorithm.
    • Significant reduction in model complexity and computational time.

    Conclusions:

    • The proposed graph theory-based method is effective for unsupervised attribute reduction in IVISs.
    • The algorithm efficiently handles large sample datasets, overcoming time complexity issues.
    • This approach provides a valuable tool for feature selection in domains with interval-valued data.