Generative Oversampling with a Contrastive Variational Autoencoder
Name
Oversampling_ICDM-1.pdf
Description
Accepted version
Size
1.88 MB
Format
Unknown
Checksum (MD5)
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Author(s)
Dai, Wangzhi
Ng, Kenney
Severson, Kristen A
Huang, Wei
Anderson, Fred
Stultz, Collin M
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
MIT-IBM Watson AI Lab
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ICDM.2019.00020
