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Generative Oversampling with a Contrastive Variational Autoencoder
Name
Oversampling_ICDM-1.pdf
Description
Accepted version
Size
1.88 MB
Format
Adobe PDF
Checksum (MD5)
dec8b8680ecfba8e9ebe0e9356377b58
Author(s)
Dai, Wangzhi
Ng, Kenney
Severson, Kristen
Huang, Wei
Anderson, Fred
Stultz, Collin
Date Issued
November 2019
Journal
Proceedings - IEEE International Conference on Data Mining, ICDM
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Dai, Wangzhi, Ng, Kenney, Severson, Kristen, Huang, Wei, Anderson, Fred et al. 2019. "Generative Oversampling with a Contrastive Variational Autoencoder." Proceedings - IEEE International Conference on Data Mining, ICDM, 2019-November.
Version
Author's final manuscript
Abstract
© 2019 IEEE. Although oversampling methods are widely used to deal with class imbalance problems, most only utilize observed samples in the minority class and ignore the rich information available in the majority class. In this work, we use an oversampling method that leverages information in both the majority and minority classes to mitigate the class imbalance problem. Experimental results on two clinical datasets with highly imbalanced outcomes demonstrate that prediction models can be significantly improved using data obtained from this oversampling method when the number of minority class samples is very small.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/ICDM.2019.00020
