Understanding data augmentation for classification: when to warp?

28 Sep 2016Sebastien C. WongAdam GattVictor StamatescuMark D. McDonnell

In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier. Two approaches for creating additional training samples are data warping, which generates additional samples through transformations applied in the data-space, and synthetic over-sampling, which creates additional samples in feature-space... (read more)

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