Techniques for Selecting the Optimal Parameters of One-Class Support Vector Machine Classifier for Reduced Samples

Yasmine Guerbai, Youcef Chibani and Yassine Meraihi
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Yasmine Guerbai: Engineering of Electrical Systems Department, University of M'Hamed Bougara Boumerdes, Algeria
Youcef Chibani: Laboratoire d'Ingénierie des Systèmes Intelligents et Communicants, FEI, USTHB, Algeria
Yassine Meraihi: Systems Engineering and Telecommunications Laboratory, University of M'Hamed Bougara Boumerdes, Algeria

International Journal of Applied Metaheuristic Computing (IJAMC), 2022, vol. 13, issue 1, 1-15

Abstract: Usually, the One-Class Support Vector Machine (OC-SVM) requires a large dataset for modeling effectively the target class independently to other classes. For finding the OC-SVM model, the available dataset is subdivided into two subsets namely training and validation, which are used for training and validating the optimal parameters. This approach is effective when a large dataset is available. However, when training samples are reduced, parameters of the OC-SVM are difficult to find in absence of the validation subset. Hence, this paper proposes various techniques for selecting the optimal parameters using only a training subset. The experimental evaluation conducted on several real-world benchmarks proves the effective use of the new selection parameter techniques for validating the model of OC-SVM classifiers versus the standard validation techniques

Date: 2022
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