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A survey of multi-label classification based on supervised and semi-supervised learning

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Abstract

Multi-label classification algorithms based on supervised learning use all the labeled data to train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label all the data needed. Multi-label classification algorithms based on semi-supervised learning can use both labeled and unlabeled data to train classifiers, resulting in better-performing models. In this paper, we first review supervised learning classification algorithms in terms of label non-correlation and label correlation and semi-supervised learning classification algorithms in terms of inductive methods and transductive methods. After that, multi-label classification algorithms are introduced from the application areas of image, text, music and video. Subsequently, evaluation metrics and datasets are briefly introduced. Finally, research directions in complex concept drift, label complex correlation, feature selection and class imbalance are presented.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (62062004), the Ningxia Natural Science Foundation Project (2022AAC03279).

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The National Nature Science Foundation of China (Grant no. 62062004); The Ningxia Natural Science Foundation Project (Grant no. 2022AAC03279).

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  1. School of Computer Science and Engineering, North Minzu University, Yinchuan, China

    Meng Han, Hongxin Wu, Zhiqiang Chen, Muhang Li & Xilong Zhang

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Han, M., Wu, H., Chen, Z. et al. A survey of multi-label classification based on supervised and semi-supervised learning. Int. J. Mach. Learn. & Cyber. 14, 697–724 (2023). https://doi.org/10.1007/s13042-022-01658-9

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