Generative Data Augmentation: Enhancing Imbalanced Data Sets with Synthetic Samples Using Conditional GANs

This paper addresses the critical issue of imbalanced datasets in machine learning, a common problem in domains such as fraud detection, medical diagnosis, and sentiment analysis. Imbalanced datasets, where one class significantly outnumbers another, can lead to biased models that perform poorly, especially on underrepresented classes. To tackle this, the authors propose a novel approach leveraging Conditional Generative Adversarial Networks (CGANs) for generating synthetic samples. The key innovation is conditioning the CGANs on class labels, enabling the generation of realistic and diverse samples for the underrepresented classes, which improves model training. Through extensive empirical evaluation across multiple benchmark datasets, the paper compares the proposed CGAN-based method with traditional oversampling techniques like SMOTE (Synthetic Minority Over-sampling Technique). The results highlight that the CGAN-based approach not only enhances model performance but also outperforms SMOTE in critical evaluation metrics such as predictive accuracy, precision, recall, and F1-score. These findings demonstrate the ability of CGANs to produce more balanced datasets, ultimately leading to more reliable and robust machine learning models. This research offers a promising solution to the challenges posed by imbalanced data and provides a pathway for improving machine learning applications in real-world, imbalanced scenarios.