Jing Huang

Adversarial Training–Based Deep Imbalanced Learning

Extensive evaluations on seven real-world financial data sets demonstrate that ATDIL outperforms state-of-the-art imbalanced learning methods across multiple metrics while exhibiting superior resilience under adversarial conditions, offering a robust and practical framework for enhancing financial fraud detection syste...