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This research introduces a fuzzy distribution modeling framework for generating synthetic tabular data that preserves causality and handles mixed data types with high interpretability. By leveraging fuzzy sets and Fuzzy Cognitive Maps, the method offers a principled alternative to conventional deep generative models, particularly where real-world data is limited or inaccessible. The approach demonstrates competitive utility and privacy metrics while enabling transparent simulation of decision-making processes through linguistic queries.
Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservation
arXiv:2609.34349v1 Announce Type: new Abstract: 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 representation. Synthetic samples are generated by sampling fuzzy concepts rather than raw values, enabling native support for mixed data types, missing values, and domain constraints. The methodology further supports linguistic queries and IF-THEN reasoning, facilitating transparent simulation of decision-making processes. Experimental results on benchmark datasets demonstrate competitive performance with respect to utility, fidelity and privacy compared to state-of-the-art methods, while offering substantially improved interpretability. These results establish fuzzy distribution modeling as a principled and effective approach for synthetic tabular data generation in fuzzy systems and decision support applications.
Authors:Michael Vasilakakis (1), Dimitris K. Iakovidis (1) ((1) Department of Computer Science and Biomedical Informatics, University of Thessaly, Lamia, Greece)
Abstract: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 representation. Synthetic samples are generated by sampling fuzzy concepts rather than raw values, enabling native support for mixed data types, missing values, and domain constraints. The methodology further supports linguistic queries and IF-THEN reasoning, facilitating transparent simulation of decision-making processes. Experimental results on benchmark datasets demonstrate competitive performance with respect to utility, fidelity and privacy compared to state-of-the-art methods, while offering substantially improved interpretability. These results establish fuzzy distribution modeling as a principled and effective approach for synthetic tabular data generation in fuzzy systems and decision support applications.
6 pages, 2 figures, 3 tables. Published in the 2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Maastricht, the Netherlands
Artificial Intelligence (cs.AI)
arXiv:2609.34349 [cs.AI]
(or arXiv:2609.34349v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.34349 arXiv-issued DOI via DataCite (pending registration)
Proc. 2026 IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), pp. 1-6
https://doi.org/10.1109/FUZZ69877.2026.11626474 DOI(s) linking to related resources
Full text under the publisher's open licence · 253 words
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