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Feature selection for support vector machines using Generalized Benders Decomposition

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  • Aytug, Haldun

Abstract

We propose an exact method, based on Generalized Benders Decomposition, to select the best M features during induction. We provide details of the method and highlight some interesting parallels between the technique proposed here and some of those published in the literature. We also propose a relaxation of the problem where selecting too many features is penalized. The original method performs well on a variety of data sets. The relaxation, though competitive, is sensitive to the penalty parameter.

Suggested Citation

  • Aytug, Haldun, 2015. "Feature selection for support vector machines using Generalized Benders Decomposition," European Journal of Operational Research, Elsevier, vol. 244(1), pages 210-218.
    • Handle: RePEc:eee:ejores:v:244:y:2015:i:1:p:210-218
    DOI: 10.1016/j.ejor.2015.01.006
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    References listed on IDEAS

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    1. Unler, Alper & Murat, Alper, 2010. "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, Elsevier, vol. 206(3), pages 528-539, November.
    2. Dunbar, Michelle & Murray, John M. & Cysique, Lucette A. & Brew, Bruce J. & Jeyakumar, Vaithilingam, 2010. "Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment," European Journal of Operational Research, Elsevier, vol. 206(2), pages 470-478, October.
    3. Pacheco, Joaquín & Casado, Silvia & Núñez, Laura, 2009. "A variable selection method based on Tabu search for logistic regression models," European Journal of Operational Research, Elsevier, vol. 199(2), pages 506-511, December.
    4. Haldun Aytug & Gary J. Koehler & Ling He, 2008. "Risk Minimization and Minimum Description for Linear Discriminant Functions," INFORMS Journal on Computing, INFORMS, vol. 20(2), pages 317-331, May.
    5. 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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    Citations

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    Cited by:

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    2. He Jiang, 2023. "Robust forecasting in spatial autoregressive model with total variation regularization," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 195-211, March.
    3. 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.
    4. Li, Libo, 2018. "Predicting online invitation responses with a competing risk model using privacy-friendly social event data," European Journal of Operational Research, Elsevier, vol. 270(2), pages 698-708.
    5. Jiang, He & Luo, Shihua & Dong, Yao, 2021. "Simultaneous feature selection and clustering based on square root optimization," European Journal of Operational Research, Elsevier, vol. 289(1), pages 214-231.
    6. 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.
    7. Oztekin, Asil & Al-Ebbini, Lina & Sevkli, Zulal & Delen, Dursun, 2018. "A decision analytic approach to predicting quality of life for lung transplant recipients: A hybrid genetic algorithms-based methodology," European Journal of Operational Research, Elsevier, vol. 266(2), pages 639-651.
    8. Gambella, Claudio & Ghaddar, Bissan & Naoum-Sawaya, Joe, 2021. "Optimization problems for machine learning: A survey," European Journal of Operational Research, Elsevier, vol. 290(3), pages 807-828.

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