Imputation of missing data with class imbalance using conditional generative adversarial networks

Missing data is a common problem faced with real-world datasets. Imputation widely used technique to estimate the missing data. State-of-the-art imputation approaches model distribution of observed approximate values. Such an approach usually models single for entire dataset, which overlooks class-specific characteristics Class-specific are especially useful when there class imbalance. We propose new method imputing based on its by adapting popular Conditional Generative Adversarial Networks (CGAN). Our Network (CGAIN) imputes using distributions, can produce best estimates tested our baseline datasets and achieved superior performance compared state-of-the-art approaches.