LAUSR

Support vector machine (SVM) and twin SVM (TWSVM) are sensitive to the noisy classification, due to the unlimited measures in their losses, especially for imbalanced classification problem. In this paper, by combining the advantages of the correntropy induced loss function (C-Loss) and the hinge loss function (hinge loss), we introduce the rescaled hinge loss function (Rhinge loss), which is a monotonic, bounded, and nonconvex loss, into TWSVM for imbalanced noisy classification, called RTBSVM. We show that the Rhinge loss could approximate the hard margin loss and the hinge loss by adjusting the rescaled parameter, and further, our RTBSVM could improve the stability and performance of TWSVM and it is effective for imbalanced noisy classification. The experimental results show that our method performs better than the compared TWSVMs and robust SVMs on the imbalanced noisy classification.