A Computationally Efficient Density-Aware Adversarial Resampling Framework Using Wasserstein GANs for Imbalance and Overlapping Data Classification

Effectively handling imbalanced datasets remains a fundamental challenge in computational modeling and machine learning, particularly when class overlap significantly deteriorates classification performance. Traditional oversampling methods often generate synthetic samples without considering density variations, leading to redundant or misleading instances that exacerbate class overlap in high-density regions. To address these limitations, we propose Wasserstein Generative Adversarial Network Variational Density Estimation WGAN-VDE, a computationally efficient density-aware adversarial resampling framework that enhances minority class representation while strategically reducing class overlap. The originality of WGAN-VDE lies in its density-aware sample refinement, ensuring that synthetic samples are positioned in underrepresented regions, thereby improving class distinctiveness. By applying structured feature representation, targeted sample generation, and density-based selection mechanisms strategies, the proposed framework ensures the generation of well-separated and diverse synthetic samples, improving class separability and reducing redundancy. The experimental evaluation on 20 benchmark datasets demonstrates that this approach outperforms 11 state-of-the-art rebalancing techniques, achieving superior results in F1-score, Accuracy, G-Mean, and AUC metrics. These results establish the proposed method as an effective and robust computational approach, suitable for diverse engineering and scientific applications involving imbalanced data classification and computational modeling.