  Data augmentation is rapidly gaining attention in machine learning. Synthetic
data can be generated by simple transformations or through the data
distribution. In the latter case, the main challenge is to estimate the label
associated to new synthetic patterns. This paper studies the effect of
generating synthetic data by convex combination of patterns and the use of
these as unsupervised information in a semi-supervised learning framework with
support vector machines, avoiding thus the need to label synthetic examples. We
perform experiments on a total of 53 binary classification datasets. Our
results show that this type of data over-sampling supports the well-known
cluster assumption in semi-supervised learning, showing outstanding results for
small high-dimensional datasets and imbalanced learning problems.
