Imbalanced Classification via a Tabular Translation GAN

• URL: http://arxiv.org/abs/2204.08683v1
• Date: Tue, 19 Apr 2022 06:02:53 GMT
• Title: Imbalanced Classification via a Tabular Translation GAN
• Authors: Jonathan Gradstein, Moshe Salhov, Yoav Tulpan, Ofir Lindenbaum, Amir Averbuch
• Abstract summary: We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresponding synthetic minority samples. We show that the proposed method improves average precision when compared to alternative re-weighting and oversampling techniques.
• Score: 4.864819846886142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When presented with a binary classification problem where the data exhibits severe class imbalance, most standard predictive methods may fail to accurately model the minority class. We present a model based on Generative Adversarial Networks which uses additional regularization losses to map majority samples to corresponding synthetic minority samples. This translation mechanism encourages the synthesized samples to be close to the class boundary. Furthermore, we explore a selection criterion to retain the most useful of the synthesized samples. Experimental results using several downstream classifiers on a variety of tabular class-imbalanced datasets show that the proposed method improves average precision when compared to alternative re-weighting and oversampling techniques.

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