Understanding Data Augmentation for Classification: When to Warp?
Understanding Data Augmentation for Classification: When to Warp?
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. We …