SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing
There has been a growing demand to access large public datasets to extract valuable insights or enhance their services. However, this also involves risks, such as privacy breaches and unauthorized data exposure. Data synthesis has emerged as a popular technique to address privacy preservation and da...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10704657/ |
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| author | Jiwoo Kim Seri Park Junsung Koh Dongha Kim |
| author_facet | Jiwoo Kim Seri Park Junsung Koh Dongha Kim |
| author_sort | Jiwoo Kim |
| collection | DOAJ |
| description | There has been a growing demand to access large public datasets to extract valuable insights or enhance their services. However, this also involves risks, such as privacy breaches and unauthorized data exposure. Data synthesis has emerged as a popular technique to address privacy preservation and data usability simultaneously. Recently, numerous methods based on deep learning have been developed, while a clear understanding of their effectiveness is still insufficient, and the necessity for more efficient frameworks persists. In this study, we propose an efficient and theoretically principled method based on a deep generative model to effectively generate high-quality synthetic tabular data. First, we introduce a novel technique called C2Smap–a learnable pre-processing method that automatically transforms continuous distributions into simpler and easily generatable forms. We then develop a conditional generative model with a hierarchical structure and its corresponding learning framework, called HCIWAE, to successfully capture imbalanced categorical distributions. Combining these two components, we coin our method Synthetic data generation with C2Smap (SynC2S). Through comprehensive experimental analyses, we demonstrate the superiority and efficiency of SynC2S in generating synthetic data compared to other recent competitors. Furthermore, as a by-product, we claim that SynC2S could be a favorable option to solve over-sampling tasks, constructing high-performance prediction models by generating synthetic data for the minority class. |
| format | Article |
| id | doaj-art-e386e59e6fd44aeb91f66fe03d7de3f5 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e386e59e6fd44aeb91f66fe03d7de3f52025-01-14T00:02:29ZengIEEEIEEE Access2169-35362025-01-01135575559410.1109/ACCESS.2024.347270610704657SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-ProcessingJiwoo Kim0Seri Park1Junsung Koh2Dongha Kim3https://orcid.org/0000-0001-7819-5619Department of Statistics, Sungshin Women’s University, Seoul, Republic of KoreaDepartment of Statistics, Sungshin Women’s University, Seoul, Republic of KoreaKohwoon and Company Corporation, Bucheon-si, Gyeonggi-do, Republic of KoreaDepartment of Statistics, Sungshin Women’s University, Seoul, Republic of KoreaThere has been a growing demand to access large public datasets to extract valuable insights or enhance their services. However, this also involves risks, such as privacy breaches and unauthorized data exposure. Data synthesis has emerged as a popular technique to address privacy preservation and data usability simultaneously. Recently, numerous methods based on deep learning have been developed, while a clear understanding of their effectiveness is still insufficient, and the necessity for more efficient frameworks persists. In this study, we propose an efficient and theoretically principled method based on a deep generative model to effectively generate high-quality synthetic tabular data. First, we introduce a novel technique called C2Smap–a learnable pre-processing method that automatically transforms continuous distributions into simpler and easily generatable forms. We then develop a conditional generative model with a hierarchical structure and its corresponding learning framework, called HCIWAE, to successfully capture imbalanced categorical distributions. Combining these two components, we coin our method Synthetic data generation with C2Smap (SynC2S). Through comprehensive experimental analyses, we demonstrate the superiority and efficiency of SynC2S in generating synthetic data compared to other recent competitors. Furthermore, as a by-product, we claim that SynC2S could be a favorable option to solve over-sampling tasks, constructing high-performance prediction models by generating synthetic data for the minority class.https://ieeexplore.ieee.org/document/10704657/Synthetic data generationcomplex-to-simple mappinghierarchically conditional importance weighted autoencoder |
| spellingShingle | Jiwoo Kim Seri Park Junsung Koh Dongha Kim SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing IEEE Access Synthetic data generation complex-to-simple mapping hierarchically conditional importance weighted autoencoder |
| title | SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing |
| title_full | SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing |
| title_fullStr | SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing |
| title_full_unstemmed | SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing |
| title_short | SynC2S: An Efficient Method for Synthesizing Tabular Data With a Learnable Pre-Processing |
| title_sort | sync2s an efficient method for synthesizing tabular data with a learnable pre processing |
| topic | Synthetic data generation complex-to-simple mapping hierarchically conditional importance weighted autoencoder |
| url | https://ieeexplore.ieee.org/document/10704657/ |
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