Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets

  • M. Peréz-Ortiz University of Cambridge
  • P. Tiňo The University of Birmingham
  • R. Mantiuk University of Cambridge
  • C. Hervás-Martínez University of Cordoba

Abstract

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.

Published
2019-07-17
Section
AAAI Technical Track: Machine Learning