发表机构
University of Thessaly（色萨利大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于模糊集理论的模糊分布建模方法，用于合成表格数据生成，通过模糊认知图建模特征依赖，支持混合数据与缺失值，在保持可解释性的同时实现与最先进方法相当的效用、保真度和隐私性能。
AI 中文摘要
当真实世界的数据有限或不可访问时，合成表格数据生成为机器学习模型的训练提供了一种有效的替代方案。然而，表格数据的异质性、非平滑性和不完整性对传统的概率生成模型和深度生成模型构成了根本性挑战，这些模型的可解释性仍然有限。本文提出了一种基于模糊集理论的新型模糊分布建模方法，用于合成表格数据生成。特征分布使用模糊集表示，特征依赖关系通过模糊认知图建模，从而产生低参数且可解释的数据表示。合成样本通过采样模糊概念而非原始值生成，从而原生支持混合数据类型、缺失值和领域约束。该方法进一步支持语言查询和IF-THEN推理，促进决策过程的透明模拟。在基准数据集上的实验结果表明，与最先进的方法相比，该方法在效用、保真度和隐私方面具有竞争力的性能，同时提供了显著改进的可解释性。这些结果确立了模糊分布建模作为模糊系统和决策支持应用中合成表格数据生成的一种原则性且有效的方法。
英文摘要
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.
Comments6 pages, 2 figures, 3 tables. Published in the 2026 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Maastricht, the Netherlands
Journal refProc. 2026 IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE), pp. 1-6
DOI：10.1109/FUZZ69877.2026.11626474
