Cost-sensitive probabilistic predictions for support vector machines

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

European Journal of Operational Research, 2024, vol. 314, issue 1, 268-279

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.

Keywords: Machine learning; Support vector machines; Probabilistic classification; Cost-sensitive classification (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:314:y:2024:i:1:p:268-279

DOI: 10.1016/j.ejor.2023.09.027

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