  Most deep learning classification studies assume clean data. However, when
dealing with the real world data, we encounter three problems such as 1)
missing data, 2) class imbalance, and 3) missing label problems. These problems
undermine the performance of a classifier. Various preprocessing techniques
have been proposed to mitigate one of these problems, but an algorithm that
assumes and resolves all three problems together has not been proposed yet. In
this paper, we propose HexaGAN, a generative adversarial network framework that
shows promising classification performance for all three problems. We interpret
the three problems from a single perspective to solve them jointly. To enable
this, the framework consists of six components, which interact with each other.
We also devise novel loss functions corresponding to the architecture. The
designed loss functions allow us to achieve state-of-the-art imputation
performance, with up to a 14% improvement, and to generate high-quality
class-conditional data. We evaluate the classification performance (F1-score)
of the proposed method with 20% missingness and confirm up to a 5% improvement
in comparison with the performance of combinations of state-of-the-art methods.
