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Integer programming models for feature selection: New extensions and a randomized solution algorithm

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  • Bertolazzi, P.
  • Felici, G.
  • Festa, P.
  • Fiscon, G.
  • Weitschek, E.

Abstract

Feature selection methods are used in machine learning and data analysis to select a subset of features that may be successfully used in the construction of a model for the data. These methods are applied under the assumption that often many of the available features are redundant for the purpose of the analysis. In this paper, we focus on a particular method for feature selection in supervised learning problems, based on a linear programming model with integer variables. For the solution of the optimization problem associated with this approach, we propose a novel robust metaheuristics algorithm that relies on a Greedy Randomized Adaptive Search Procedure, extended with the adoption of short memory and a local search strategy. The performances of our heuristic algorithm are successfully compared with those of well-established feature selection methods, both on simulated and real data from biological applications. The obtained results suggest that our method is particularly suited for problems with a very large number of binary or categorical features.

Suggested Citation

  • Bertolazzi, P. & Felici, G. & Festa, P. & Fiscon, G. & Weitschek, E., 2016. "Integer programming models for feature selection: New extensions and a randomized solution algorithm," European Journal of Operational Research, Elsevier, vol. 250(2), pages 389-399.
    • Handle: RePEc:eee:ejores:v:250:y:2016:i:2:p:389-399
    DOI: 10.1016/j.ejor.2015.09.051
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    References listed on IDEAS

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    2. Piramuthu, Selwyn, 2004. "Evaluating feature selection methods for learning in data mining applications," European Journal of Operational Research, Elsevier, vol. 156(2), pages 483-494, July.
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    Cited by:

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    2. Zhang, Yishi & Zhu, Ruilin & Chen, Zhijun & Gao, Jie & Xia, De, 2021. "Evaluating and selecting features via information theoretic lower bounds of feature inner correlations for high-dimensional data," European Journal of Operational Research, Elsevier, vol. 290(1), pages 235-247.
    3. Li, An-Da & He, Zhen & Wang, Qing & Zhang, Yang, 2019. "Key quality characteristics selection for imbalanced production data using a two-phase bi-objective feature selection method," European Journal of Operational Research, Elsevier, vol. 274(3), pages 978-989.
    4. Manlio Gaudioso & Giovanni Giallombardo & Giovanna Miglionico, 2023. "Sparse optimization via vector k-norm and DC programming with an application to feature selection for support vector machines," Computational Optimization and Applications, Springer, vol. 86(2), pages 745-766, November.
    5. Ghaddar, Bissan & Naoum-Sawaya, Joe, 2018. "High dimensional data classification and feature selection using support vector machines," European Journal of Operational Research, Elsevier, vol. 265(3), pages 993-1004.
    6. Daehan Won & Hasan Manzour & Wanpracha Chaovalitwongse, 2020. "Convex Optimization for Group Feature Selection in Networked Data," INFORMS Journal on Computing, INFORMS, vol. 32(1), pages 182-198, January.
    7. Douek-Pinkovich, Yifat & Ben-Gal, Irad & Raviv, Tal, 2022. "The stochastic test collection problem: Models, exact and heuristic solution approaches," European Journal of Operational Research, Elsevier, vol. 299(3), pages 945-959.
    8. Giovanni Felici & Kumar Parijat Tripathi & Daniela Evangelista & Mario Rosario Guarracino, 2017. "A mixed integer programming-based global optimization framework for analyzing gene expression data," Journal of Global Optimization, Springer, vol. 69(3), pages 727-744, November.
    9. Yifat Douek-Pinkovich & Irad Ben-Gal & Tal Raviv, 2021. "The generalized test collection problem," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 372-386, July.

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