Cost-sensitive probabilistic predictions for support vector machines

• URL: http://arxiv.org/abs/2310.05997v1
• Date: Mon, 9 Oct 2023 11:00:17 GMT
• Title: Cost-sensitive probabilistic predictions for support vector machines
• Authors: Sandra Ben\'itez-Pe\~na, Rafael Blanquero, Emilio Carrizosa, Pepa Ram\'irez-Cobo
• Abstract summary: Support vector machines (SVMs) are widely used and constitute one of the best examined and used machine learning models. We propose a novel approach to generate probabilistic outputs for the SVM.
• Score: 1.743685428161914
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. In this paper we propose a novel approach to generate probabilistic outputs for the SVM. The new method has the following three properties. First, it is designed to be cost-sensitive, and thus the different importance of sensitivity (or true positive rate, TPR) and specificity (true negative rate, TNR) is readily accommodated in the model. As a result, the model can deal with imbalanced datasets which are common in operational business problems as churn prediction or credit scoring. Second, the SVM is embedded in an ensemble method to improve its performance, making use of the valuable information generated in the parameters tuning process. Finally, the probabilities estimation is done via bootstrap estimates, avoiding the use of parametric models as competing approaches. Numerical tests on a wide range of datasets show the advantages of our approach over benchmark procedures.

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