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Solving a class of feature selection problems via fractional 0–1 programming

Author

Listed:
  • Erfan Mehmanchi

    (University of Pittsburgh)

  • Andrés Gómez

    (University of Southern California)

  • Oleg A. Prokopyev

    (University of Pittsburgh)

Abstract

Feature selection is a fundamental preprocessing step for many machine learning and pattern recognition systems. Notably, some mutual-information-based and correlation-based feature selection problems can be formulated as fractional programs with a single ratio of polynomial 0–1 functions. In this paper, we study approaches that ensure globally optimal solutions for these feature selection problems. We conduct computational experiments with several real datasets and report encouraging results. The considered solution methods perform well for medium- and reasonably large-sized datasets, where the existing mixed-integer linear programs from the literature fail.

Suggested Citation

  • Erfan Mehmanchi & Andrés Gómez & Oleg A. Prokopyev, 2021. "Solving a class of feature selection problems via fractional 0–1 programming," Annals of Operations Research, Springer, vol. 303(1), pages 265-295, August.
    • Handle: RePEc:spr:annopr:v:303:y:2021:i:1:d:10.1007_s10479-020-03917-w
    DOI: 10.1007/s10479-020-03917-w
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    References listed on IDEAS

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