Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification

This disproportion often leads to biased model training, making the classifier inclined towards predicting the majority class in the inference phase[1, 2]. The class imbalance problem cannot be readily overlooked, as many real-world datasets related to critical tasks, such as those used in the medical field for disease identification, the finance sector for fraud detection, and network intrusion datasets used in cyber security, exhibit such asymmetric class distributions [3, 4, 5]. Existing machine learning and deep learning approaches primarily utilize resampling techniques to tackle class imbalance which involves adjustment techniques to balance the class distribution in datasets [6, 7]. Among diverse resampling techniques, Oversampling approaches are commonly preferred for addressing class imbalance mainly due to their inherent ability to equalize the class distribution while preserving data semantics and achieving superior performance. There has been a plethora of different oversampling techniques proposed in the literature, ranging from traditional approaches [8, 9, 10, 11, 12] to those based on deep learning [13, 14, 15].