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A survey of class-imbalanced semi-supervised learning

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Abstract

Semi-supervised learning(SSL) can substantially improve the performance of deep neural networks by utilizing unlabeled data when labeled data is scarce. The state-of-the-art(SOTA) semi-supervised algorithms implicitly assume that the class distribution of labeled datasets and unlabeled datasets are balanced, which means the different classes have the same numbers of training samples. However, they can hardly perform well on minority classes when the class distribution of training data is imbalanced. Recent work has found several ways to decrease the degeneration of semi-supervised learning models in class-imbalanced learning. In this article, we comprehensively review class-imbalanced semi-supervised learning (CISSL), starting with an introduction to this field, followed by a realistic evaluation of existing class-imbalanced semi-supervised learning algorithms and a brief summary of them.

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Data availability

The datasets are the benchmark datasets available online.

Code availability

The codes will be available from the corresponding author upon request.

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Funding

This work is supported by National Natural Science Foundation of China (62072326), and the Key Research and Development Plan of Shanxi Province No. 201903D421007 and No. 202102010101004.

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  1. School of Information and Computer, Taiyuan University of Technology, Taiyuan, 030024, Shanxi, People’s Republic of China

    Qian Gui, Hong Zhou, Na Guo & Baoning Niu

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  1. Qian Gui
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  2. Hong Zhou
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  3. Na Guo
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  4. Baoning Niu
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Contributions

QG: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing-original draft, Writing-review and editing, Visualization. HZ: Validation, Formal analysis. NG: Validation, Formal analysis. BN: Resources, Writing-review and editing, Supervision, Funding acquisition.

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Correspondence to Baoning Niu.

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Gui, Q., Zhou, H., Guo, N. et al. A survey of class-imbalanced semi-supervised learning. Mach Learn (2023). https://doi.org/10.1007/s10994-023-06344-7

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