SOS: Score-based Oversampling for Tabular Data

Jayoung Kim, Chaejeong Lee, Yehjin Shin, Sewon Park, Minjung Kim, Noseong Park, Jihoon Cho

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationKDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Number of pages11
ISBN (Electronic)9781450393850
Publication statusPublished - 2022 Aug 14
Event28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States
Duration: 2022 Aug 142022 Aug 18

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining


Conference28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/TerritoryUnited States

Bibliographical note

Funding Information:
Jayoung Kim and Chaejeong Lee equally contributed. Noseong Park is the corresponding author. This work was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (10% from No. 2020-0-01361, Artificial Intelligence Graduate School Program at Yonsei University and 70% from No. 2021-0-00231, Development of Approximate DBMS Query Technology to Facilitate Fast Query Processing for Exploratory Data Analysis) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (20% from No. 2021R1F1A1063981).

Publisher Copyright:
© 2022 ACM.

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems


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