Online Support Vector Machine with a Single Pass for Streaming Data

Lisha Hu, Chunyu Hu (), Zheng Huo, Xinlong Jiang and Suzhen Wang
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Lisha Hu: Institute of Information Technology, Hebei University of Economics and Business, Shijiazhuang 050061, China
Chunyu Hu: School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
Zheng Huo: Institute of Information Technology, Hebei University of Economics and Business, Shijiazhuang 050061, China
Xinlong Jiang: Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
Suzhen Wang: Institute of Information Technology, Hebei University of Economics and Business, Shijiazhuang 050061, China

Mathematics, 2022, vol. 10, issue 17, 1-24

Abstract: In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement.

Keywords: online learning; support vector machine; minimum enclosing ball (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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