Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
By Justin Engelmann, Stefan Lessmann. Expert systems with applications, 2020
315 citations recorded by the source corpus.
Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models. Popular countermeasures include oversampling the minority class. Standard methods like SMOTE rely on finding nearest neighbours and linear interpolations which are problematic in case of high-dimensional, complex data distributions. Generative Adversarial Networks (GANs) have been proposed as an alternative method for generating artificial minority examples as they can model complex distributions. However, prior research on GAN-based oversampling does not incorporate recent advancements from the literature on generating realistic tabular data with GANs. Previous studies also focus on numerical variables whereas categorical features are common in many business applications of classification methods such as credit scoring. The paper propoes an oversampling method based on a conditional Wasserstein GAN that can effectively model tabular datasets with numerical and categorical variables and pays special attention to the down-stream classification task through an auxiliary classifier loss. We benchmark our method against standard oversampling methods and the imbalanced baseline on seven real-world datasets. Empirical results evidence the competitiveness of GAN-based oversampling.
Subfields: Accounting, Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/c0eb5b9365d95e30347f597ad3baa8c2f55773a5
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Citations in the corpus
- Deep Neural Networks and Tabular Data: A Survey (2021, background): “This is where data generation plays a crucial role, as one of the standard deployment steps involves the imputation of missing values [34], [35] and the rebalancing of the dataset [36], [37] (i.e., equalizing sample sizes for different classes).”
- TabDDPM: Modelling Tabular Data with Diffusion Models (2022, background): “The recent works have developed a large number of models, including tabular VAEs (Xu et al., 2019) and GAN-based approaches (Xu et al., 2019; Engelmann & Lessmann, 2021; Jordon et al., 2018; Fan et al., 2020; Torfi et al., 2022; Zhao et al., 2021; Kim et al., 2021; Zhang et al., 2021; Nock &…”
- CTAB-GAN: Effective Table Data Synthesizing (2021, methodology): “CW-GAN [6] applies the Wasser-stein distance [1] into the conditional GAN framework.”