In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep learning from data with imbalanced class distributions. DVAE is designed to alleviate the class imbalance by explicitly learning class boundaries between training samples, and uses learned class boundaries to guide the feature learning and sample generation. To learn class boundaries, DVAE learns a latent two-component mixture distributor, conditioned by the class labels, so the latent features can help differentiate minority class vs. majority class samples. In order to balance the training data for deep learning to emphasize on the minority class, we combine DVAE and generative adversarial networks (GAN) to form a unified model, DVAAN, which generates synthetic instances close to the class boundaries as training data to learn latent features and update the model. Experiments and comparisons confirm that DVAAN significantly alleviates the class imbalance and delivers accurate models for deep learning from imbalanced data.
Global-and-Local Aware Data Generation for the Class Imbalance Problem
In many real-world classification applications such as fake
news detection, the training data can be extremely imbalanced, which brings challenges to existing classifiers as the
majority classes dominate the loss functions of classifiers.
Oversampling techniques such as SMOTE are effective
approaches to tackle the class imbalance problem by
producing more synthetic minority samples. Despite their
success, the majority of existing oversampling methods only
consider local data distributions when generating minority
samples, which can result in noisy minority samples that do
not fit global data distributions or interleave with majority
classes. Hence, in this paper, we study the class imbalance
problem by simultaneously exploring local and global data
information since: (i) the local data distribution could give
detailed information for generating minority samples; and
(ii) the global data distribution could provide guidance to
avoid generating outliers or samples that interleave with
majority classes. Specifically, we propose a novel framework
GL-GAN, which leverages the SMOTE method to explore
local distribution in a learned latent space and employs
GAN to capture the global information, so that synthetic
minority samples can be generated under even extremely
imbalanced scenarios. Experimental results on diverse real
data sets demonstrate the effectiveness of our GL-GAN
framework in producing realistic and discriminative minority samples for improving the classification performance of
various classifiers on imbalanced training data. Our code is
available at https://github.com/wentao-repo/GL-GAN.
- Award ID(s):
- 1909702
- Publication Date:
- NSF-PAR ID:
- 10167748
- Journal Name:
- Proceedings of the SIAM International Conference on Data Mining
- Page Range or eLocation-ID:
- 307-315
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1137/1.9781611976236.35
