CTTVAE：面向不平衡数据集的条件表格数据生成的潜在空间结构
CTTVAE: Latent Space Structuring for Conditional Tabular Data Generation on Imbalanced Datasets
AI总结：
CTTVAE通过潜在空间结构和目标采样策略，提升不平衡数据集下条件表格数据生成的下游实用性。
摘要：
在严重类别不平衡的领域中生成合成表格数据对于决策制定至关重要。然而，大多数生成模型要么忽视少数群体，要么无法产生对下游学习有用的样本。我们介绍了CTTVAE，一种基于变换器的条件表格变分自编码器，配备了两种互补机制：（i）一种类感知三元组边际损失，通过重构潜在空间实现更紧密的类内紧凑性和更清晰的类间分离；（ii）一种基于采样的训练策略，能够适应性地增加对少数群体的暴露。这些组件共同构成了CTTVAE+TBS框架，该框架能够持续产生更具代表性和实用性更强的样本，而不会破坏训练稳定性。在六个现实世界基准测试中，CTTVAE+TBS在少数类上实现了最强的下游实用性，通常超过在原始不平衡数据上训练的模型，同时保持竞争性的保真度，并弥补了基于插值采样方法和深度生成方法之间的差距。消融研究进一步证实，潜在空间结构和目标采样都对这些收益有贡献。通过明确优先考虑少数类别下的下游性能，CTTVAE+TBS为条件表格数据生成提供了一个稳健且可解释的解决方案，直接适用于医疗保健、欺诈检测和预测维护等行业，其中即使在少数类别上微小的收益也可能是关键。
英文摘要：
Generating synthetic tabular data under severe class imbalance is essential for domains where rare but high-impact events drive decision-making. However, most generative models either overlook minority groups or fail to produce samples that are useful for downstream learning. We introduce CTTVAE, a Conditional Transformer-based Tabular Variational Autoencoder equipped with two complementary mechanisms: (i) a class-aware triplet margin loss that restructures the latent space for sharper intra-class compactness and inter-class separation, and (ii) a training-by-sampling strategy that adaptively increases exposure to underrepresented groups. Together, these components form CTTVAE+TBS, a framework that consistently yields more representative and utility-aligned samples without destabilizing training. Across six real-world benchmarks, CTTVAE+TBS achieves the strongest downstream utility on minority classes, often surpassing models trained on the original imbalanced data while maintaining competitive fidelity and bridging the gap for privacy for interpolation-based sampling methods and deep generative methods. Ablation studies further confirm that both latent structuring and targeted sampling contribute to these gains. By explicitly prioritizing downstream performance in rare categories, CTTVAE+TBS provides a robust and interpretable solution for conditional tabular data generation, with direct applicability to industries such as healthcare, fraud detection, and predictive maintenance where even small gains in minority cases can be critical.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
