  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 experimentally evaluate the benefits of data augmentation for a
convolutional backpropagation-trained neural network, a convolutional support
vector machine and a convolutional extreme learning machine classifier, using
the standard MNIST handwritten digit dataset. We found that while it is
possible to perform generic augmentation in feature-space, if plausible
transforms for the data are known then augmentation in data-space provides a
greater benefit for improving performance and reducing overfitting.
