Optimized customer churn prediction using tabular generative adversarial network (GAN)-based hybrid sampling method and cost-sensitive learning

This study conducted an experiment with six comparative sampling methods, six datasets, and three machine learning algorithms. The results show that CostLearnGAN achieved a satisfying result across all evaluation metrics with a 1.44 average mean rank score. Additionally, this study provided a robustness measurement for algorithms, demonstrating that CostLearnGAN outperforms other sampling methods in improving the performance of classical machine learning models with a 5.68 robustness value on average.