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

Erfan Mehmanchi (), Andrés Gómez () and Oleg A. Prokopyev ()
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Erfan Mehmanchi: University of Pittsburgh
Andrés Gómez: University of Southern California
Oleg A. Prokopyev: University of Pittsburgh

Annals of Operations Research, 2021, vol. 303, issue 1, No 13, 265-295

Abstract: 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.

Keywords: Feature selection; Fractional 0–1 programming; Mixed-integer linear programming; Parametric algorithms (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10479-020-03917-w

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