  Class imbalance is a common problem in supervised learning and impedes the
predictive performance of classification models. Popular countermeasures
include oversampling the minority class. Standard methods like SMOTE rely on
finding nearest neighbours and linear interpolations which are problematic in
case of high-dimensional, complex data distributions. Generative Adversarial
Networks (GANs) have been proposed as an alternative method for generating
artificial minority examples as they can model complex distributions. However,
prior research on GAN-based oversampling does not incorporate recent
advancements from the literature on generating realistic tabular data with
GANs. Previous studies also focus on numerical variables whereas categorical
features are common in many business applications of classification methods
such as credit scoring. The paper propoes an oversampling method based on a
conditional Wasserstein GAN that can effectively model tabular datasets with
numerical and categorical variables and pays special attention to the
down-stream classification task through an auxiliary classifier loss. We
benchmark our method against standard oversampling methods and the imbalanced
baseline on seven real-world datasets. Empirical results evidence the
competitiveness of GAN-based oversampling.
