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Related Experiment Video

Updated: May 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

The relevance sample-feature machine: a sparse Bayesian learning approach to joint feature-sample selection.

Yalda Mohsenzadeh, Hamid Sheikhzadeh, Ali M Reza

    IEEE Transactions on Cybernetics
    |June 21, 2013
    PubMed
    Summary
    This summary is machine-generated.

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    This study presents a new machine learning algorithm, relevance sample feature machine (RSFM), for effective feature selection in classification. RSFM simultaneously identifies relevant features and data samples, improving accuracy and reducing complexity.

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Statistical Learning

    Background:

    • Traditional machine learning algorithms often struggle with high-dimensional data and identifying truly relevant features.
    • Existing methods like the standard Relevance Vector Machine (RVM) achieve sparsity primarily in the sample domain.

    Purpose of the Study:

    • To introduce a novel sparse Bayesian machine learning algorithm for embedded feature and sample selection.
    • To enhance classification and regression tasks by simultaneously identifying relevant features and data samples.

    Main Methods:

    • Developed the Relevance Sample Feature Machine (RSFM), a novel algorithm extending the standard RVM.
    • Employed a separable model in feature and sample domains with a Bayesian approach and Gaussian priors.

    Related Experiment Videos

    Last Updated: May 10, 2026

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
    07:35

    Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

    Published on: October 11, 2018

  • Achieved sparsity in both feature and sample domains through the learned model.

    • Main Results:

    • RSFM demonstrated success in feature selection by effectively eliminating irrelevant features.
    • The algorithm achieved accurate classification performance on synthetic and benchmark datasets.
    • Experimental results confirmed RSFM's ability to perform simultaneous feature and sample selection.

    Conclusions:

    • RSFM offers a robust solution for embedded feature selection in classification and regression.
    • The algorithm leads to reduced system complexity, improved generalization, and avoidance of overfitting.
    • RSFM presents a computationally efficient approach, particularly during the testing phase.