This systematic survey reviews data balancing methods for imbalanced datasets and concludes that no single technique is universally superior, with choice depending on data traits, classifier, and metrics.
Wasserstein generative adversarial network with gradient penalty for handwritten digit generation
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Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods
This systematic survey reviews data balancing methods for imbalanced datasets and concludes that no single technique is universally superior, with choice depending on data traits, classifier, and metrics.