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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Semisupervised Feature Selection Based on Relevance and Redundancy Criteria.

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    |January 24, 2017
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    This study introduces a novel semisupervised feature selection method, balancing feature relevance and redundancy. The proposed method, Pearson

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    Area of Science:

    • Machine Learning
    • Data Science
    • Computer Science

    Background:

    • Feature selection is crucial for enhancing classification accuracy and reducing computational load.
    • Balancing feature relevance and redundancy is challenging, particularly when labeled data is scarce or expensive.
    • Existing methods often focus solely on supervised or unsupervised approaches, limiting their effectiveness in semisupervised scenarios.

    Purpose of the Study:

    • To propose a novel semisupervised feature selection method that effectively balances feature relevance and redundancy.
    • To introduce a new criterion, max-relevance and min-redundancy based on Pearson's correlation coefficient (RRPC), for optimal feature subset selection.
    • To evaluate the performance of the proposed RRPC method against established supervised, unsupervised, and semisupervised feature selection techniques.

    Main Methods:

    • Developed a semisupervised feature selection approach utilizing a max-relevance and min-redundancy criterion based on Pearson's correlation coefficient (RRPC).
    • Employed an incremental search technique for efficient selection of optimal feature subsets.
    • Validated the method on benchmark datasets with both binary and multicategory data.

    Main Results:

    • The proposed RRPC method demonstrated a superior ability to balance feature relevance and redundancy in semisupervised settings.
    • Comparative studies showed RRPC outperforming classic supervised (mRMR, Fisher score), unsupervised (Laplacian score), and other semisupervised (sSelect, locality sensitive) methods.
    • Experimental results confirmed the effectiveness of RRPC in achieving improved classification performance with reduced feature sets.

    Conclusions:

    • The RRPC criterion offers an effective solution for semisupervised feature selection, addressing the challenge of limited labeled data.
    • This method successfully integrates supervised relevance with unsupervised redundancy constraints for robust feature selection.
    • RRPC provides a valuable tool for improving classification models in scenarios where acquiring labeled data is a significant obstacle.