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A Mixed Integer Linear Programming Support Vector Machine for Cost-Effective Group Feature Selection: Branch-Cut-and-Price Approach

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  • Lee, In Gyu
  • Yoon, Sang Won
  • Won, Daehan
Abstract
Recently, cost-based feature selection has received significant attention due to its great ability to achieve promising prediction accuracy at a minimum feature acquisition cost. To further improve its predictive and economic performances, this research proposes a cost-effective 1-norm support vector machine with group feature selection as GFS-CESVM1. Its robust counterpart model, GFS-RCESVM1, is also introduced to address the cost uncertainty of features and feature groups because cost variation commonly exists in real-world problems. The proposed models are formulated as Mixed Integer Linear Programming (MILP). To efficiently solve the proposed SVM MILP models, we develop a Branch-Cut-and-Price (BCP) algorithm that considers only a limited number of variables and/or constraints, which thereby leads to rapid convergence to an optimal solution. Various experimental results on benchmark and synthetic datasets demonstrate that GFS-CESVM1 can achieve competitive outcomes by considering not only individual feature evaluation but also group structural information among features. The GFS-RCESVM1 can identify the subset of features that is immune to cost uncertainty and therefore provide feasible and optimal solutions. Furthermore, our BCP algorithm can dominantly outperform the ordinary BB algorithm for finding better objective value and integrality gap within a short period of time.

Suggested Citation

  • Lee, In Gyu & Yoon, Sang Won & Won, Daehan, 2022. "A Mixed Integer Linear Programming Support Vector Machine for Cost-Effective Group Feature Selection: Branch-Cut-and-Price Approach," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1055-1068.
    • Handle: RePEc:eee:ejores:v:299:y:2022:i:3:p:1055-1068
    DOI: 10.1016/j.ejor.2021.12.030
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    References listed on IDEAS

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    1. Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
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    4. Maldonado, Sebastián & Pérez, Juan & Bravo, Cristián, 2017. "Cost-based feature selection for Support Vector Machines: An application in credit scoring," European Journal of Operational Research, Elsevier, vol. 261(2), pages 656-665.
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    Cited by:

    1. Maggioni, Francesca & Spinelli, Andrea, 2025. "A novel robust optimization model for nonlinear Support Vector Machine," European Journal of Operational Research, Elsevier, vol. 322(1), pages 237-253.
    2. Labbé, Martine & Landete, Mercedes & Leal, Marina, 2023. "Dendrograms, minimum spanning trees and feature selection," European Journal of Operational Research, Elsevier, vol. 308(2), pages 555-567.
    3. Ma, Xuejiao & Che, Tianqi & Jiang, Qichuan, 2025. "A three-stage prediction model for firm default risk: An integration of text sentiment analysis," Omega, Elsevier, vol. 131(C).
    4. Noriyoshi Sukegawa & Shohei Suzuki & Yoshiko Ikebe & Yoshito Hirata, 2024. "On Computing Medians of Marked Point Process Data Under Edit Distance," Journal of Optimization Theory and Applications, Springer, vol. 200(1), pages 178-193, January.
    5. Kusunoki, Yoshifumi & Tatsumi, Keiji, 2025. "Multi-class support vector machine based on minimization of reciprocal-geometric-margin norms," European Journal of Operational Research, Elsevier, vol. 324(2), pages 580-589.

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