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Cost-sensitive probabilistic predictions for support vector machines

Author

Listed:
  • 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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