A comparative analysis of data augmentation approaches for improved minority behavior detection in digital games

Previous research in behavioral- and game analytics showed that data augmentation plays a crucial role against the challenges of detecting minority entities (e.g. premium or retaining users) in behavioral datasets. By putting more emphasis on the minority entities, data augmentation allows us to utilize existing solutions without the need for extensive adjustments. In this study, we build upon previous work in this area by providing a comparison from both a methodology perspective and a data alteration perspective. The comparison focuses on three methods: Synthetic Minority Oversampling Technique (a nearest neighbor based approach), Variational Autoencoders, and Generative Adversarial Networks (both deep learning based approaches). We conduct an empirical evaluation using retention prediction in a freemium mobile game. Our findings indicate that each method offers advantages in terms of improved generalization results for different evaluation measures.