Abstract
Universum twin support vector machine (\( \mathfrak {U} \)-TSVM) is an efficient method for binary classification problems . In this paper, we improve the \( \mathfrak {U} \)-TSVM algorithm and propose an improved Universum twin bounded support vector machine (named as IUTBSVM) . Indeed, by introducing a different Lagrangian function for the primal problems, we obtain new dual formulations so that we do not need to compute inverse matrices. Also to reduce the computational time of the proposed method, we suggest smaller size of the rectangular kernel matrices than the other methods. Numerical experiments on several UCI benchmark data sets indicate that the IUTBSVM is more efficient than the other three algorithms, namely \(\mathfrak {U}\)-SVM, TSVM, and \(\mathfrak {U}\)-TSVM in sense of the classification accuracy.
The authors were supported by the Czech Science Foundation Grant P403-18-04735S.
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Moosaei, H., Hladík, M. (2021). Inverse Free Universum Twin Support Vector Machine. In: Simos, D.E., Pardalos, P.M., Kotsireas, I.S. (eds) Learning and Intelligent Optimization. LION 2021. Lecture Notes in Computer Science(), vol 12931. Springer, Cham. https://doi.org/10.1007/978-3-030-92121-7_21
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