Abstract
In supervised learning, the Universum, a third class that is not a part of either class in the classification task, has proven to be useful. In this study we propose (N\( \mathfrak {U} \)TBSVM), a Newton-based approach for solving in the primal space the optimization problems related to Twin Bounded Support Vector Machines with Universum data (\( \mathfrak {U} \)TBSVM). In the N\( \mathfrak {U} \)TBSVM, the constrained programming problems of \( \mathfrak {U} \)TBSVM are converted into unconstrained optimization problems, and a generalization of Newton’s method for solving the unconstrained problems is introduced. Numerical experiments on synthetic, UCI, and NDC data sets show the ability and effectiveness of the proposed N\( \mathfrak {U} \)TBSVM. We apply the suggested method for gender detection from face images, and compare it with other methods.
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Data Availability
The data that support the findings of this study are available from the UCI machine learning repository, MC-NDC data sets, Face image data sets, associated with the following links: https://archive.ics.uci.edu/ml/index.php, https://github.com/dmusican/ndc, http://cswww.essex.ac.uk/mv/allfaces/index.html.
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Funding
H. Moosaei’s research was funded by the Center for Foundations of Modern Computer Science (Charles Univ. project UNCE/SCI/004) and the Czech Science Foundation Grant 22-19353S. The work of M. Hladík was supported by the Czech Science Foundation Grant P403-22-11117S. M.R. Guarracino’s work has been partially funded by the BiBiNet project (H35F21000430002) within POR-Lazio FESR 2014-2020, and conducted within the framework of the Basic Research Program at the National Research University Higher School of Economics (HSE).
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Moosaei, H., Khosravi, S., Bazikar, F. et al. A novel method for solving universum twin bounded support vector machine in the primal space. Ann Math Artif Intell (2023). https://doi.org/10.1007/s10472-023-09896-5
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DOI: https://doi.org/10.1007/s10472-023-09896-5