Imbalanced Classification via Explicit Gradient Learning From Augmented Data

Learning from imbalanced tabular data is a significant challenge in real-world classification tasks. In such cases, neural network performance is substantially impaired due to implicit bias toward the majority class. Existing solutions attempt to eliminate the bias through data re-sampling or re-weighting the loss in the learning process. Still, these methods tend to overfit the minority samples and perform poorly when the structure of the minority class is highly irregular. Here, we propose a novel deep meta-learning technique to augment a given imbalanced dataset with new minority instances. These additional data are incorporated during the classifier's training process, and their contributions are learned explicitly. The augmented samples are modified throughout the training to optimize the classifiers' average-precision score on a validation set. Multiple experiments with synthetic and real-world imbalanced datasets demonstrate the advantage of the proposed method, leading to a significant gap in comparison to many existing baselines.