GEM-T:通过合适的时刻生成表格数据
Miao Li1, Phuc Nguyen1, Christopher Tam1
1Department of Pathology at Beth Israel Deaconess Medical Center (BIDMC), Boston, MA 02215 during the completion of this work.
ArXiv
|December 8, 2025
概括
我们开发了GEM-T,这是一种使用最大 (MaxEnt) 来生成合成表格数据的新方法. 在大多数数据集上,GEM-T的性能优于深度学习模型,为结构化数据生成提供轻量级,高性能解决方案.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 表格式数据很普遍,但对于生成模型来说具有挑战性,特别是在有限或敏感信息的情况下.
- 现有的生成模型与表格数据集中的异质性和复杂相互作用作斗争.
研究的目的:
- 引入GEM-T (表格生成最大化),一种用于合成表格数据生成的新方法.
- 为了证明GEM-T在捕获高阶列交互和处理各种数据类型方面的有效性.
主要方法:
- 该研究利用最大 (MaxEnt) 的原理来开发GEM-T模型.
- 在训练数据的列中,GEM-T直接模拟n次序交互 (例如,对对,三次).
- 数据预处理涉及适当的转换,以揭示低维相关性.
主要成果:
- 在68%的不同公共数据集中,GEM-T与最先进的深度神经网络方法相匹配或超越 (34中的23).
- 与深度学习方法相比,该模型需要的可训练参数要少得多.
- GEM-T有效地处理异质数据类型 (连续,离散,分类) 和缺乏局部结构.
结论:
- GEM-T为生成合成结构化数据提供了一个有前途,轻量级和高性能的替代方案.
- 最大原则为表格数据中复杂相互作用的建模提供了一个强大的框架.
- 这些发现表明,表格数据中的有价值信息通常存在于低维的相关性中,可以通过适当的转换来访问.
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