Open Access

ARTICLE

A Novel Semi-Supervised Multi-Label Twin Support Vector Machine

Qing Ai^1,2,*, Yude Kang¹, Anna Wang²

1 School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China
2 College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

* Corresponding Author: Qing Ai. Email:

Intelligent Automation & Soft Computing 2021, 27(1), 205-220. https://doi.org/10.32604/iasc.2021.013357

Received 04 August 2020; Accepted 25 September 2020; Issue published 07 January 2021

Abstract

Multi-label learning is a meaningful supervised learning task in which each sample may belong to multiple labels simultaneously. Due to this characteristic, multi-label learning is more complicated and more difficult than multi-class classification learning. The multi-label twin support vector machine (MLTSVM) [], which is an effective multi-label learning algorithm based on the twin support vector machine (TSVM), has been widely studied because of its good classification performance. To obtain good generalization performance, the MLTSVM often needs a large number of labelled samples. In practical engineering problems, it is very time consuming and difficult to obtain all labels of all samples for multi-label learning problems, so we can only obtain a large number of partially labelled and unlabelled samples and a small number of labelled samples. However, the MLTSVM can use only expensive labelled samples and ignores inexpensive partially labelled and unlabelled samples. Because of the MLTSVM’s disadvantages, we propose an alternative novel semi-supervised multi-label twin support vector machine, named SS-MLTSVM, which can take full advantage of the geometric information of the edge distribution embedded in partially labelled and unlabelled samples by introducing a manifold regularization term into each sub-classifier and use the successive overrelaxation (SOR) method to speed up the solving process. Experimental results on several publicly available benchmark multi-label datasets show that, compared with the classical MLTSVM, our proposed SS-MLTSVM has better classification performance.

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Citations