Score-based generative models (SGMs) are a recent breakthrough in generating fake images. SGMs are known to surpass other generative models, e.g., generative adversarial networks (GANs) and variational autoencoders (VAEs). Being inspired by their big success, in this work, we fully customize them for generating fake tabular data. In particular, we are interested in oversampling minor classes since imbalanced classes frequently lead to sub-optimal training outcomes. To our knowledge, we are the first presenting a score-based tabular data oversampling method. Firstly, we re-design our own score network since we have to process tabular data. Secondly, we propose two options for our generation method: the former is equivalent to a style transfer for tabular data and the latter uses the standard generative policy of SGMs. Lastly, we define a fine-tuning method, which further enhances the oversampling quality. In our experiments with 6 datasets and 10 baselines, our method outperforms other oversampling methods in all cases.
|Title of host publication
|KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
|Association for Computing Machinery
|Number of pages
|Published - 2022 Aug 14
|28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States
Duration: 2022 Aug 14 → 2022 Aug 18
|Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
|28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
|22/8/14 → 22/8/18
Bibliographical notePublisher Copyright:
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Information Systems