Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservation
Synthetic tabular data generation provides an effective alternative for the training of machine learning models when real-world data is limited or inaccessible. However, the heterogeneous, non-smooth, and incomplete nature of tabular data poses fundamental challenges to conventional probabilistic and deep generative models, where their interpretability remains limited. This paper proposes a novel fuzzy distribution modeling methodology for synthetic tabular data generation based on fuzzy sets theory. Feature distributions are represented using fuzzy sets and feature dependencies are modeled through Fuzzy Cognitive Maps, resulting in a low-parameter, and an interpretable data representatio...
| Authors | Michael Vasilakakis, Dimitris K. Iakovidis |
|---|---|
| Primary category | cs.AI |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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