GAIN: Missing Data Imputation using Generative Adversarial Nets. We propose a novel method for imputing missing data by adapting the well-known Generative Adversarial Nets (GAN) framework. Accordingly, we call our method Generative Adversarial Imputation Nets (GAIN). The generator (G) observes some components of a real data vector, imputes the missing components conditioned on what is actually observed, and outputs a completed vector. The discriminator (D) then takes a completed vector and attempts to determine which components were actually observed and which were imputed. To ensure that D forces G to learn the desired distribution, we provide D with some additional information in the form of a hint vector. The hint reveals to D partial information about the missingness of the original sample, which is used by D to focus its attention on the imputation quality of particular components. This hint ensures that G does in fact learn to generate according to the true data distribution. We tested our method on various datasets and found that GAIN significantly outperforms state-of-the-art imputation methods.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Kirchmeyer, Matthieu; Gallinari, Patrick; Rakotomamonjy, Alain; Mantrach, Amin: Unsupervised domain adaptation with non-stochastic missing data (2021)
- Qian, Zhaozhi; Alaa, Ahmed M.; van der Schaar, Mihaela: CPAS: the UK’s national machine learning-based hospital capacity planning system for COVID-19 (2021)
- van der Schaar, Mihaela; Alaa, Ahmed M.; Floto, Andres; Gimson, Alexander; Scholtes, Stefan; Wood, Angela; McKinney, Eoin; Jarrett, Daniel; Lio, Pietro; Ercole, Ari: How artificial intelligence and machine learning can help healthcare systems respond to COVID-19 (2021)
- Zhang, Ying; Zhou, Baohang; Cai, Xiangrui; Guo, Wenya; Ding, Xiaoke; Yuan, Xiaojie: Missing value imputation in multivariate time series with end-to-end generative adversarial networks (2021)
- Biessmann, Felix; Rukat, Tammo; Schmidt, Phillipp; Naidu, Prathik; Schelter, Sebastian; Taptunov, Andrey; Lange, Dustin; Salinas, David: DataWig: missing value imputation for tables (2019)