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Cost-sensitive probabilistic predictions for support vector machines

Author

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  • Benítez-Peña, Sandra
  • Blanquero, Rafael
  • Carrizosa, Emilio
  • Ramírez-Cobo, Pepa

Abstract

Support vector machines (SVMs) are widely used and constitute one of the best examined and used machine learning models for two-class classification. Classification in SVM is based on a score procedure, yielding a deterministic classification rule, which can be transformed into a probabilistic rule (as implemented in off-the-shelf SVM libraries), but is not probabilistic in nature. On the other hand, the tuning of the regularization parameters in SVM is known to imply a high computational effort and generates pieces of information that are not fully exploited, not being used to build a probabilistic classification rule.

Suggested Citation

  • Benítez-Peña, Sandra & Blanquero, Rafael & Carrizosa, Emilio & Ramírez-Cobo, Pepa, 2024. "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, Elsevier, vol. 314(1), pages 268-279.
    • Handle: RePEc:eee:ejores:v:314:y:2024:i:1:p:268-279
    DOI: 10.1016/j.ejor.2023.09.027
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    References listed on IDEAS

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    1. De Bock, Koen W. & Coussement, Kristof & Lessmann, Stefan, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," European Journal of Operational Research, Elsevier, vol. 285(2), pages 612-630.
    2. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
    3. Koen W. de Bock & Kristof Coussement & Stefan Lessmann, 2020. "Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach," Post-Print hal-02863245, HAL.
    4. K.W. de Bock & D. van den Poel, 2012. "Reconciling performance and interpretability in customer churn prediction modeling using ensemble learning based on generalized additive models," Post-Print hal-00800148, HAL.
    5. Benítez-Peña, Sandra & Carrizosa, Emilio & Guerrero, Vanesa & Jiménez-Gamero, M. Dolores & Martín-Barragán, Belén & Molero-Río, Cristina & Ramírez-Cobo, Pepa & Romero Morales, Dolores & Sillero-Denami, 2021. "On sparse ensemble methods: An application to short-term predictions of the evolution of COVID-19," European Journal of Operational Research, Elsevier, vol. 295(2), pages 648-663.
    6. Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
    7. Karatzoglou, Alexandros & Meyer, David & Hornik, Kurt, 2006. "Support Vector Machines in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 15(i09).
    8. Sandra Benítez-Peña & Rafael Blanquero & Emilio Carrizosa & Pepa Ramírez-Cobo, 2019. "On support vector machines under a multiple-cost scenario," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 663-682, September.
    9. Maldonado, Sebastián & Domínguez, Gonzalo & Olaya, Diego & Verbeke, Wouter, 2021. "Profit-driven churn prediction for the mutual fund industry: A multisegment approach," Omega, Elsevier, vol. 100(C).
    10. Wang, Lifeng & Shen, Xiaotong, 2007. "On L1-Norm Multiclass Support Vector Machines: Methodology and Theory," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 583-594, June.
    11. Keramati, Abbas & Ardabili, Seyed M.S., 2011. "Churn analysis for an Iranian mobile operator," Telecommunications Policy, Elsevier, vol. 35(4), pages 344-356, May.
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