集成插补-分类用于缺失数据的监督学习
Integrated Imputation-Classification for Supervised Learning with Missing Data
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中文总结 AI 辅助
针对缺失特征值的监督分类，提出集成插补与分类网络（IICN），联合训练插补器和(n+1)类判别器，实现贝叶斯最优分类，在多个数据集上优于现有方法。
中文摘要 AI 辅助
我们研究带有缺失特征值的监督分类问题。现有方法通常将插补与分类解耦，产生的插补可能看似合理但对预测无信息量。相反，我们提出了集成插补与分类网络（IICN），该网络通过单一类别监督分类目标，对抗性地联合训练一个插补器和一个(n+1)类判别器，其中判别器学习区分n个真实类别和一个额外的“插补”类别。我们证明在全局最优情况下，插补器和判别器共同实现了对缺失坐标的边际化，并产生贝叶斯最优分类器。我们在FashionMNIST、CIFAR-10以及具有自然缺失的表格数据集上评估了IICN。IICN优于经典的先插补后分类流程和最近的生成式基线，在具有挑战性的设置中显示出强大的鲁棒性和准确性。
英文摘要
We study supervised classification problems with missing feature values. Existing approaches often decouple imputation from classification, producing imputations that may be plausible but uninformative for prediction. Instead, we propose the Integrated Imputation and Classification Network (IICN), which jointly trains an imputer and an ${(n{+}1)}$-classdiscriminator adversarially with a single class supervised classification objective, where the discriminator learns to distinguish among the $n$ true classes and an additional ``imputed" class. We prove that at the global optimum, the imputer and discriminator together implement marginalization over missing coordinates and yield a Bayes-optimal classifier. We evaluate IICN on FashionMNIST, CIFAR-10, and tabular datasets with naturally occurring missingness. IICN outperforms classical impute-then-classify pipelines and recent generative baselines, showing strong robustness and accuracy in challenging settings.
发表机构
- University of Toronto（多伦多大学）
- TELUS
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