HexaGAN: Generative Adversarial Nets for Real World Classification

Most deep learning classification studies assume clean data. However, dirty data is prevalent in real world, and this undermines the classification performance. The data we practically encounter has problems such as 1) missing data, 2) class imbalance, and 3) missing label. Preprocessing techniques assume one of these problems and mitigate it, but an algorithm that assumes all three problems and resolves them has not yet been proposed. Therefore, in this paper, we propose HexaGAN, a generative adversarial network (GAN) framework that shows good classification performance for all three problems. We interpret the three problems from a similar perspective to solve them jointly. To enable this, the framework consists of six components, which interact in an end-to-end manner. We also devise novel loss functions corresponding to the architecture. The designed loss functions achieve state-of-the-art imputation performance with up to a 14% improvement and 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 combinations of state-of-the-art methods.