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Extensions to Online Feature Selection Using Bagging and Boosting.

Gregory Ditzler, Joseph LaBarck, James Ritchie

    IEEE Transactions on Neural Networks and Learning Systems
    |October 14, 2017
    PubMed
    Summary
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    This study introduces an ensemble of online linear models for efficient feature subset selection in large datasets. The new method improves prediction accuracy while maintaining model sparsity and complexity.

    Area of Science:

    • Machine Learning
    • Data Mining
    • Computational Statistics

    Background:

    • Feature subset selection is crucial for identifying informative variables in large datasets.
    • Current methods struggle with high-dimensional data and large instance volumes due to resource constraints.
    • Online feature selection (OFS) offers a promising approach using partial feature information.

    Purpose of the Study:

    • To extend online feature selection (OFS) by developing an ensemble of online linear models.
    • To address limitations of existing feature selection techniques in handling high-dimensional and large-scale data.
    • To improve prediction accuracy and maintain model sparsity and complexity.

    Main Methods:

    • Developed an ensemble of online linear models based on the OFS approach.

    Related Experiment Videos

  • Utilized a linear model as the base classifier with $l_{0}$-norm constraint for feature selection.
  • Constrained the $l_{0}$-norm of the parameter vector to achieve sparse linear models.

    • Main Results:

    • The proposed ensemble model typically yields a smaller error rate compared to individual linear models.
    • The ensemble approach maintains the same level of sparsity and complexity as single models.
    • Demonstrated effective feature selection for large-scale and high-dimensional datasets.

    Conclusions:

    • Ensemble of online linear models is an effective extension of OFS for large-scale data.
    • The method provides improved predictive performance with controlled model complexity.
    • This approach offers a scalable solution for feature selection in resource-constrained environments.