通过缺失模式与上下文信息提升表格数据填补效果
CACTI: Leveraging Copy Masking and Contextual Information to Improve Tabular Data Imputation
- 采用中位数截断复制掩码策略捕捉缺失规律
- 在非随机缺失下提升7.8%的R²表现
- 适合处理带语义信息的复杂表格数据
我们提出CACTI，一种基于掩码自编码的表格数据填补方法，利用缺失模式结构和上下文信息。该方法采用新颖的中位数截断复制掩码训练策略，使模型学习真实缺失模式，同时结合列名和文本描述捕捉特征间的语义关系，以更好表示特征依赖。双重归纳偏置使CACTI在多种数据集和缺失条件下均优于现有最优方法：在缺失非随机、随机和完全随机情况下，平均R²分别提升13.4%、6.1%和5.3%，整体超越次优方法7.8%。结果表明，利用特定数据集的上下文信息和缺失模式可显著提升填补性能。
原文摘要 · Abstract (English)
We present CACTI, a masked autoencoding approach for imputing tabular data that leverages the structure in missingness patterns and contextual information. Our approach employs a novel median truncated copy masking training strategy that encourages the model to learn from empirical patterns of missingness while incorporating semantic relationships between features - captured by column names and text descriptions - to better represent feature dependence. These dual sources of inductive bias enable CACTI to outperform state-of-the-art methods - an average $R^2$ gain of 7.8% over the next best method (13.4%, 6.1%, and 5.3% under missing not at random, at random and completely at random, respectively) - across a diverse range of datasets and missingness conditions. Our results highlight the value of leveraging dataset-specific contextual information and missingness patterns to enhance imputation performance.
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