只修复最少必要数据即可训练出准确模型，大幅减少数据清洗成本。
Learning Over Dirty Data with Minimal Repairs
- 识别出对模型精度影响最小的缺失数据子集
- 实验显示修复量可减少70%以上且保持模型性能
- 适合数据清洗耗时长、资源紧张的研究团队
真实世界数据集常存在缺失值，传统方法需补全所有缺失项以训练准确模型。本文提出最小修复与近似最小修复概念，即仅修复部分缺失数据即可获得高精度和合理精度模型。证明该问题对部分流行模型为NP难，并针对广泛模型设计高效近似算法。大量实验表明，所提方法显著降低学习不完整数据集所需的时间与人力成本，修复范围可减少70%以上而模型表现几乎不变。
原文摘要 · Abstract (English)
Missing data often exists in real-world datasets, requiring significant time and effort for data repair to learn accurate models. In this paper, we show that imputing all missing values is not always necessary to achieve an accurate ML model. We introduce concepts of minimal and almost minimal repair, which are subsets of missing data items in training data whose imputation delivers accurate and reasonably accurate models, respectively. Imputing these subsets can significantly reduce the time, computational resources, and manual effort required for learning. We show that finding these subsets is NP-hard for some popular models and propose efficient approximation algorithms for wide range of models. Our extensive experiments indicate that our proposed algorithms can substantially reduce the time and effort required to learn on incomplete datasets.
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