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Data augmentation forms the cornerstone of many modern machine learningtraining pipelines; yet, the mechanisms by which it works are not clearlyunderstood. Much of the research on data augmentation (DA) has focused onimproving existing techniques, examining its regularization effects in thecontext of neural network over-fitting, or investigating its impact onfeatures. Here, we undertake a holistic examination of the effect of DA onthree different classifiers, convolutional neural networks, support vectormachines, and logistic regression models, which are commonly used in supervisedclassification of imbalanced data. We support our examination with testing onthree image and five tabular datasets. Our research indicates that DA, whenapplied to imbalanced data, produces substantial changes in model weights,support vectors and feature selection; even though it may only yield relativelymodest changes to global metrics, such as balanced accuracy or F1 measure. Wehypothesize that DA works by facilitating variances in data, so that machinelearning models can associate changes in the data with labels. By diversifyingthe range of feature amplitudes that a model must recognize to predict a label,DA improves a model's capacity to generalize when learning with imbalanceddata.

Towards Understanding How Data Augmentation Works with Imbalanced Data