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Neural Computing and Applications

Erschienen in:

04.03.2022 | S.I.: NCAA 2021

Sparse discriminant twin support vector machine for binary classification

verfasst von: Xiaohan Zheng, Li Zhang, Leilei Yan

Erschienen in: Neural Computing and Applications | Ausgabe 19/2022

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Abstract

For a binary classification problem, twin support vector machine (TSVM) has a faster learning speed than support vector machine (SVM) by seeking a pair of nonparallel hyperplanes. However, TSVM has two deficiencies: poor discriminant ability and poor sparsity. To relieve them, we propose a novel sparse discriminant twin support vector machine (SD-TSVM). Inspired by the idea of the Fisher criterion, maximizing the between-class scatter and minimizing the within-class scatter, SD-TSVM introduces twin Fisher regularization terms, which may improve the discriminant ability of SD-TSVM. Moreover, SD-TSVM has a good sparsity by utilizing both the 1-norm of model coefficients and the hinge loss. Thus, SD-TSVM can efficiently perform data reduction. Classification results on nine real-world datasets show that SD-TSVM has a satisfactory performance compared with related methods.

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Titel: Sparse discriminant twin support vector machine for binary classification
verfasst von: Xiaohan Zheng
Li Zhang
Leilei Yan
Publikationsdatum: 04.03.2022
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 19/2022
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07001-1

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