Adaptive robust Adaboost-based twin support vector machine with universum data

Universum, as third class that does not belong to the positive class and negative class, allows to incorporate the prior knowledge into the learning process. A lot of reaserchers confirmed that Universum is helpful in the supervised and semi-supervised learning. Moreover, Universum has already been introduced into the support vector machine (SVM) to enhance the generalisation performance. The twin support vector machine (TSVM) serves as an updated classification algorithm based on SVM, having a fast calcula-tion speed. So we introduce Universum into TWSVM to improve the generalisation perfor-mance in the same way. Furthermore, in order to make the generalisation performance better in complex environment, an adaptive robust Adaboost-based twin support vector machine with universum learning (ARABUTWSVM) is put up in this paper. First of all, uni-versum learning is used in TSVM to settle a matter of universum data. In order to make our method more robust, we use various loss functions selected by adaptive parameter h and an update metric induced by correntropy method for distance measure. Further, so as to make further improvement of the learning effect of our method, the Adaboost method is embeded to ARABUTWSVM. The comprehensive experimentation has been performed on various datasets compared with previous universum learning methods, which show that ARABUTWSVM is an adaptive and robust method.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper introduces the role of Universum in supervised and semi-supervised learning and incorporates it into TWSVM to improve generalization performance. To enhance generalization performance in complex environments, an adaptive robust Adaboost-based twin support vector machine with universum learning (ARABUTWSVM) is proposed.