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Semi-supervised active learning algorithm for SVMs based on QBC and tri-training

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Abstract

For the problem that large-scale labeled samples are not easy to acquire in the course of Support Vector Machines (SVMs) training, a Semi-Supervised Active Learning Algorithm for SVMs (QTB-ASVM) is proposed in the paper, which efficiently combines the semi–supervised learning based on Tri-Training and active learning based on Query By Committee (QBC) with SVMs. With this method, QBC active learning is used to select the samples which are the most valuable to current SVM classifier, and Tri-Training is used to exploit useful information that remains in the unlabeled samples. The experimental results show that the proposed approach can considerably reduce the labeled samples and costs compared to the SVMs which is either not applied with semi-supervised learning or active learning or applied with only one of them, and at the same time it can ensure that the accurate classification performance is kept as the passive SVM, while improving generalization performance and also expediting the SVM training.

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Acknowledgement

The authors wish to express their gratitude to the referees for their helpful comments and kind suggestions in revising this paper. This work is substantially supported by grants from the Natural Science Foundation of China (Nos. 71701209, 72071209), and the Natural Science Foundation of Shaanxi Province of China (Nos. 2019JQ-250), and the China Postdoctoral Science Foundation funded project (2017M613415, 2019M653962).

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  1. Air and Missile Defense College, Air Force Engineering University, Shaanxi, 710051, Xi’an, People’s Republic of China

    Hailong Xu, Longyue Li & Pengsong Guo

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  1. Hailong Xu
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  2. Longyue Li
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  3. Pengsong Guo
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Correspondence to Longyue Li.

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Xu, H., Li, L. & Guo, P. Semi-supervised active learning algorithm for SVMs based on QBC and tri-training. J Ambient Intell Human Comput 12, 8809–8822 (2021). https://doi.org/10.1007/s12652-020-02665-w

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