arXiv cs.AI· Pegah Golchian, Jan Kapar, David S. Watson, Marvin N. Wright·· 12 小时前AI 评分24
MissARF：基于对抗随机森林的缺失值插补方法
Missing value imputation with adversarial random forests - MissARF
AI 导读
研究者提出缺失值插补方法 MissARF，基于对抗随机森林（ARF）做密度估计与数据合成，通过以非缺失值为条件从估计的条件分布中采样完成插补，同时支持单次与多重插补。实验显示其插补质量与当前最先进的单次、多重插补方法相当，运行速度快，且多重插补无额外开销。该方法已发表于 Statistics in Medicine。
正文 · 原文
Abstract: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.
| Subjects: | Machine Learning (stat.ML); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2507.15681 [stat.ML] |
| (or arXiv:2507.15681v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2507.15681 arXiv-issued DOI via DataCite |
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| Journal reference: | Statistics in Medicine, 45(3-5), e70379 (2026) |
| Related DOI: | https://doi.org/10.1002/sim.70379
DOI(s) linking to related resources |
Submission history
From: Jan Kapar [view email]
[v1]
Mon, 21 Jul 2025 14:44:51 UTC (6,940 KB)
[v2]
Fri, 16 Oct 2026 14:05:07 UTC (6,971 KB)
来源：arXiv cs.AI · arxiv.org