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A systematic review of generative adversarial imputation network in missing data imputation

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Abstract

Data missing has always occurred in data processing. To solve this problem, researchers have improved the process methods of the missing data with diverse strategies, which range from directly deleting missing data samples to using artificial intelligence technology to filling in incomplete data. The processing methods of the missing data have been improved. Generative adversarial imputation network (GAIN) is a kind of neural network which has an excellent performance in missing data imputation. A number of publications that research and cite the GAIN model show a significant growth trend after GAIN was proposed in 2018. GAIN has been studied and improved by many scholars in their specific fields. However, few studies have systematically surveyed the GAIN model's development trends on missing data from its birth to the present, which result in a lack of comprehensive information about GAINs general performance in different fields. In this review, we summarize the development of the GAIN model in missing data imputation from 2018 to 2022. Based on the WOS database, 32 publications are selected according to the PRISMA statement. The outcome of this paper is from the following aspects: (1) analyzing the publication information and application fields quantitatively; (2) expounding the GAIN-based models, classification, and research trends; (3) elaborating the model attributes and missing data mechanism; and (4) summarizing the existing issues and proposing the future directions. Above all, this paper can help scholars gain further insight into the missing data issues and better understand the optimized directions of GAIN models.

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Data availability

The data that support the findings of this study are available from the Web of Science repository but restrictions apply to the availability of these data, which were used under licence from Beijing Jiaotong University, and so are not publicly available. Data are, however, available from the authors upon reasonable request and with permission of Beijing Jiaotong University.

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China with Grant Number 62173025, a major project of National Social Science Foundation of China with Grant Number 18ZDA086, and Beijing Natural Science Foundation with Grant Number of L201003. The authors have no relevant financial or non-financial interests to disclose.

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  1. School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, China

    Yuqing Zhang, Runtong Zhang & Butian Zhao

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Correspondence to Runtong Zhang.

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Zhang, Y., Zhang, R. & Zhao, B. A systematic review of generative adversarial imputation network in missing data imputation. Neural Comput & Applic 35, 19685–19705 (2023). https://doi.org/10.1007/s00521-023-08840-2

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