Data Balancing Strategies: A Survey of Resampling and Augmentation Methods
It provides a comprehensive overview for researchers and practitioners dealing with imbalanced datasets, but it is incremental as it synthesizes existing methods without introducing novel contributions.
This paper surveys various data balancing strategies, including resampling and augmentation methods, to address the problem of imbalanced data in machine learning, which often leads to skewed predictions and reduced model accuracy, by reviewing and classifying techniques without presenting new experimental results.
Imbalanced data poses a significant obstacle in machine learning, as an unequal distribution of class labels often results in skewed predictions and diminished model accuracy. To mitigate this problem, various resampling strategies have been developed, encompassing both oversampling and undersampling techniques aimed at modifying class proportions. Conventional oversampling approaches like SMOTE enhance the representation of the minority class, whereas undersampling methods focus on trimming down the majority class. Advances in deep learning have facilitated the creation of more complex solutions, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which are capable of producing high-quality synthetic examples. This paper reviews a broad spectrum of data balancing methods, classifying them into categories including synthetic oversampling, adaptive techniques, generative models, ensemble-based strategies, hybrid approaches, undersampling, and neighbor-based methods. Furthermore, it highlights current developments in resampling techniques and discusses practical implementations and case studies that validate their effectiveness. The paper concludes by offering perspectives on potential directions for future exploration in this domain.