Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning

The paper considers credit-related decisions in the financial industry, which heavily relies on ML for decision support. Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models. Popular countermeasures include oversampling the minority class.

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Justin Engelmann, Stefan Lessmann (2024). Dataset: Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning. https://doi.org/10.57702/rr0z5let

DOI retrieved: December 16, 2024

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Created December 16, 2024
Last update December 16, 2024
Author Justin Engelmann
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Stefan Lessmann