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Exploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets

Accepted version
Peer-reviewed

Type

Conference Object

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Authors

Pérez-Ortiz, M 
Tino, P 
Hervás-Martínez, C 

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.

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Keywords

Journal Title

33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019

Conference Name

AAAI Conference on Artificial Intelligence

Journal ISSN

2159-5399
2374-3468

Volume Title

Publisher