Enhancing Classification Performance on Imbalanced Data by Combining Autoencoder Generative Adversarial Networks with Synthetic Minority Over-Sampling Technique-Particle Swarm Optimization
Shang-Er Juan, Yan-Yu Lin, Liang-Sian Lin, Chien‐Hsin Lin, Hsin-Yu Chang, Jhao-Sin Lai · 2026
Class imbalance is a common issue in machine learning, often causing bias in learning models toward the majority class and leading to poor predictive performance for the minority class data. To address the class imbalance problem, this paper presents the autoencoder-based generative adversarial network-synthetic minority over-sampling technique-particle swarm optimization (AEGAN-SMOTE-PSO) model. We compared the AEGAN-SMOTE-PSO model with three other state-of-the-art oversampling techniques. Those experimental results demonstrate that the AEGAN-SMOTE-PSO model effectively improves the classification performance of two support vector machine prediction models on two imbalanced medical cases. Compared to the other three oversampling methods, the AEGAN-SMOTE-PSO model effectively provides satisfactory predictive performance in terms of recall, precision, and F1-score.