2020/12/23 by Wei Qiu, Yangsibo Huang, Qiu, Wei +3
Computer Science · Mathematics · #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Statistical Methods and Inference #Stochastic Gradient Optimization Techniques #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2012.12581
Wei Qiu and Yangsibo Huang contribute equally to this work
arxiv created 2020/12/23 · openalex publication_date 2020/12/23 · arxiv updated 2020/12/24 · openalex created_date 2021/01/05 · openalex updated_date 2026/07/28
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.