Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
This addresses data scarcity and imbalance issues in machine learning, particularly for binary classification tasks, but is incremental as it builds on existing semi-supervised and data augmentation methods.
The paper tackles the problem of small and imbalanced datasets by using synthetic data generated from convex combinations of patterns in a semi-supervised learning framework with support vector machines, avoiding the need to label synthetic examples. Results on 53 binary classification datasets show outstanding performance for small high-dimensional and imbalanced problems, supporting the cluster assumption.
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.