用对抗随机森林实现快速高精度缺失值填补
Missing value imputation with adversarial random forests -- MissARF
- 基于对抗随机森林估计条件分布并采样填补
- 单次与多次填补效果媲美顶尖方法，速度极快
- 适合生物统计等需高效精准填补的场景
缺失值处理是生物统计分析中的常见挑战，通常通过插补方法解决。我们提出一种新颖、快速且易用的插补方法——缺失值对抗随机森林（MissARF），基于生成式机器学习，支持单次与多次插补。MissARF利用对抗随机森林（ARF）进行密度估计与数据合成，对缺失值的观测样本，基于非缺失值条件，从ARF生成的条件分布中采样以完成填补。实验表明，MissARF在插补质量上与当前最优的单次和多次插补方法相当，同时具备极快运行速度，且多次插补无需额外计算成本。
原文摘要 · Abstract (English)
Handling missing values is a common challenge in biostatistical analyses, typically addressed by imputation methods. We propose a novel, fast, and easy-to-use imputation method called missing value imputation with adversarial random forests (MissARF), based on generative machine learning, that provides both single and multiple imputation. MissARF employs adversarial random forest (ARF) for density estimation and data synthesis. To impute a missing value of an observation, we condition on the non-missing values and sample from the estimated conditional distribution generated by ARF. Our experiments demonstrate that MissARF performs comparably to state-of-the-art single and multiple imputation methods in terms of imputation quality and fast runtime with no additional costs for multiple imputation.
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