IFGAN: Missing Value Imputation using Feature-specific Generative Adversarial Networks
This work provides an improved method for missing value imputation, which is beneficial for data scientists and researchers working with incomplete datasets, offering an incremental improvement over existing techniques.
This paper addresses the problem of missing value imputation in datasets. The authors propose IFGAN, a method that uses feature-specific Generative Adversarial Networks to impute missing values, demonstrating superior performance compared to existing state-of-the-art algorithms on real-life datasets under various missing conditions.
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.