An Efficient Angle-based Universum Least Squares Twin Support Vector Machine for Classification

Abstract

Universum-based support vector machine incorporates prior information about the distribution of data in training of the classifier. This leads to better generalization performance but with increased computation cost. Various twin hyperplane-based models are proposed to reduce the computation cost of universum-based algorithms. In this work, we present an efficient angle-based universum least squares twin support vector machine (AULSTSVM) for classification. This is a novel approach of incorporating universum in the formulation of least squares-based twin SVM model. First, the proposed AULSTSVM constructs a universum hyperplane, which is proximal to universum data points. Then, the classifying hyperplane is constructed by minimizing the angle with the universum hyperplane. This gives prior information about data distribution to the classifier. In addition to the quadratic loss, we introduce linear loss in the optimization problem of the proposed AULSTSVM, which leads to lesser computation cost of the model. Numerical experiments are performed on several benchmark synthetic, real-world, and large-scale datasets. The results show that proposed AULSTSVM performs better than existing algorithms w.r.t. generalization performance as well as computation time. Moreover, an application to Alzheimer’s disease is presented, where AULSTSVM obtains accuracy of 95% for classification of healthy and Alzheimers subjects. The results imply that the proposed AULSTSVM is a better alternative for classification of large-scale datasets and biomedical applications.