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

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. [ABSTRACT FROM AUTHOR]