Comparative Study of Data Generation Magnitudes in Oversampling Techniques: Synthetic Minority Over-Sampling Technique and Generative Adversarial Network

Class imbalance in datasets is a common issue across various fields, including banking, medicine, and information security. Data augmentation is a frequently used approach to address this problem by generating additional samples of the minority class to rebalance the dataset. Other studies have employed methods such as Generative Adversarial Networks (GAN) and Synthetic Minority Over-sampling Technique (SMOTE)for this purpose. Therefore, this study aims to compare the differences between the two oversampling techniques, GAN and SMOTE, in handling class imbalance problems. The results of this study show the accuracy of distinguishing between real and generated data to determine which method offers a greater advantage. The method demonstrates better performance in multi-class classification tasks. The GAN model can be effectively applied to both binary classification and the generation of diverse samples from minority and majority classes, even in extreme cases where the number of minority samples is tiny. Moreover, in terms of classification accuracy and the quality of generated samples, GAN outperforms SMOTE in data augmentation and oversampling. It maintains strong performance even when the number of instances in the minority class is limited.