Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification - Vipavee Damminsed; Wanida Panup; Rabian Wangkeeree (IEEE Access, 2023)
Semi-supervised learning utilizes labeled data and the geometric information in the unlabeled data to construct a model whereas supervised learning makes use of the only label data. So, semi-supervised learning establishes a more reasonable classifier. In recent years, the Laplacian support vector machine (Lap-SVM) has received a lot of interest in the framework of semi-supervised classification. To develop the performance of Lap-SVM, Laplacian twin support vector machine (Lap-TSVM) has shown exceptional performance as an addition to improve the computational complexity. However, dealing with noise sensitivity and instability for resampling due to its hinge loss function is still a challenge. In this paper, we provide a novel Laplacian twin support vector machine by combining pinball loss function, termed as Lap-PTSVM, to effectively handle the aforementioned problems. As a result, it improves a better generalization ability of the classifier. Several experiments have been performed on artificial and UCI datasets. The results show that the proposed model has noise insensitivity comparable to the Lap-TSVM and has a great generalization performance. Furthermore, non-parametric statistical test are also conducted to justify the competitive performance of Lap-PTSVM.