Exploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets
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Publication Date
2019Journal 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
ISSN
2159-5399
ISBN
9781577358091
Pages
4715-4722
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Pérez-Ortiz, M., Tino, P., Mantiuk, R., & Hervás-Martínez, C. (2019). Exploiting synthetically generated data with semi-supervised learning for small and imbalanced datasets. 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, 4715-4722. https://doi.org/10.17863/CAM.36472
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.
Identifiers
External DOI: https://doi.org/10.17863/CAM.36472
This record's URL: https://www.repository.cam.ac.uk/handle/1810/289210
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