提出分层生成框架，让合成表格数据更好保留字段间依赖关系。
Dependency-aware synthetic tabular data generation
- 先独立生成无关字段，再按规则重建相关字段
- 在6种模型上显著提升函数依赖与逻辑依赖保留率
- 适合医疗等隐私敏感领域对数据结构真实性的需求
合成表格数据在医疗等隐私敏感领域应用日益广泛，但现有生成模型常无法保留特征间的关联关系。尤其是函数依赖（FDs）和逻辑依赖（LDs）——即特征间的确定性与规则性关系——在合成数据中常被忽略或表现不佳。为填补这一空白，我们提出分层特征生成框架（HFGF）。我们构建了包含已知依赖关系的基准数据集以评估该方法。框架首先使用任意标准生成模型生成独立特征，再依据预设的FD和LD规则重建依赖特征。在四个不同规模、特征不平衡度和依赖复杂度的基准数据集上的实验表明，HFGF在包括CTGAN、TVAE和GReaT在内的六种生成模型上均显著提升了FD和LD的保留效果。结果证明，HFGF能有效增强合成表格数据的结构保真度与下游任务实用性。
原文摘要 · Abstract (English)
Synthetic tabular data is increasingly used in privacy-sensitive domains such as health care, but existing generative models often fail to preserve inter-attribute relationships. In particular, functional dependencies (FDs) and logical dependencies (LDs), which capture deterministic and rule-based associations between features, are rarely or often poorly retained in synthetic datasets. To address this research gap, we propose the Hierarchical Feature Generation Framework (HFGF) for synthetic tabular data generation. We created benchmark datasets with known dependencies to evaluate our proposed HFGF. The framework first generates independent features using any standard generative model, and then reconstructs dependent features based on predefined FD and LD rules. Our experiments on four benchmark datasets with varying sizes, feature imbalance, and dependency complexity demonstrate that HFGF improves the preservation of FDs and LDs across six generative models, including CTGAN, TVAE, and GReaT. Our findings demonstrate that HFGF can significantly enhance the structural fidelity and downstream utility of synthetic tabular data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。