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

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Áreas de investigación:
Año:
2019
Tipo de publicación:
Artículo en conferencia
Autores:
Título del libro:
Proceedings of the Thirty-Third AAAI (Association for the Advancement of Artificial Intelligence) Conference on Artificial Intelligence (AAAI'19)
Páginas:
4715-4722
Organización:
Honolulu,Hawaii, USA
Mes:
27th February
ISBN:
978-1-57735-809-1
ISSN:
2159-5399
BibTex:
Abstract:
Data augmentation is rapidly gaining attention in machinelearning. Synthetic data can be generated by simple transfor-mations or through the data distribution. In the latter case,the main challenge is to estimate the label associated to newsynthetic patterns. This paper studies the effect of generat-ing synthetic data by convex combination of patterns and theuse of these as unsupervised information in a semi-supervisedlearning framework with support vector machines, avoidingthus the need to label synthetic examples. We perform ex-periments on a total of 53 binary classification datasets. Ourresults show that this type of data over-sampling supportsthe well-known cluster assumption in semi-supervised learn-ing, showing outstanding results for small high-dimensionaldatasets and imbalanced learning problems.
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