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