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

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 p...

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Bibliographic Details
Published inAnnals of operations research Vol. 303; no. 1-2; pp. 265 - 295
Main Authors Mehmanchi, Erfan, Gómez, Andrés, Prokopyev, Oleg A.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.08.2021
Springer
Springer Nature B.V
Subjects
Business and Management
Combinatorics
Datasets
Feature selection
Functions (mathematics)
Linear programming
Machine learning
Mathematical programming
Mathematical research
Mixed integer
Operations research
Operations Research/Decision Theory
Pattern recognition
Pattern recognition systems
Polynomials
S.I.: Data Mining and Decision Analytics
Theory of Computation
United States
Fractional 0–1 programming
Feature selection
Parametric algorithms
Mixed-integer linear programming
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Summary: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.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-020-03917-w