  Missing value imputation is a challenging and well-researched topic in data
mining. In this paper, we propose IFGAN, a missing value imputation algorithm
based on Feature-specific Generative Adversarial Networks (GAN). Our idea is
intuitive yet effective: a feature-specific generator is trained to impute
missing values, while a discriminator is expected to distinguish the imputed
values from observed ones. The proposed architecture is capable of handling
different data types, data distributions, missing mechanisms, and missing
rates. It also improves post-imputation analysis by preserving inter-feature
correlations. We empirically show on several real-life datasets that IFGAN
outperforms current state-of-the-art algorithm under various missing
conditions.
