GSVM: An SVM for handling imbalanced accuracy between classes inbi-classification problems

• A new support vector machine has been designed.
• This model provides a more balanced accuracy between classes in a classification problem.
• This model is the most appropriate in order to manage on imbalanced accuracy.
• The results are very promising.

A new support vector machine, SVM, is introduced, called GSVM, which is specially designed for bi-classification problems where balanced accuracy between classes is the objective. Starting from a standard SVM, the GSVM is obtained from a low-cost post-processing strategy by modifying the initial bias. Thus, the bias for GSVM is calculated by moving the original bias in the SVM to improve the geometric mean between the true positive rate and the true negative rate. The proposed solution neither modifies the original optimization problem for SVM training, nor introduces new hyper-parameters. Experimentation carried out on a high number of databases (23) shows GSVM obtaining the desired balanced accuracy between classes. Furthermore, its performance improves well-known cost-sensitive schemes for SVM, without adding complexity or computational cost.

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