标题： HMVI：利用自然邻域统一异构属性进行缺失值推断 显示英文标题

标题： HMVI: Unifying Heterogeneous Attributes with Natural Neighbors for Missing Value Inference

摘要： 缺失值填补是机器智能中的一个基本挑战，严重依赖数据的完整性。 当前的填补方法通常独立处理数值和类别属性，忽视了异构特征之间的关键相互依赖关系。 为了解决这些限制，我们提出了一种新颖的填补方法，在统一框架中显式建模跨类型特征依赖关系。 我们的方法利用完整和不完整的实例，以确保表格数据中准确且一致的填补。 大量实验结果表明，所提出的方法在现有技术上表现更优，并显著提升了下游机器学习任务，为具有缺失数据的实际系统提供了一个稳健的解决方案。 显示英文摘要

摘要： Missing value imputation is a fundamental challenge in machine intelligence, heavily dependent on data completeness. Current imputation methods often handle numerical and categorical attributes independently, overlooking critical interdependencies among heterogeneous features. To address these limitations, we propose a novel imputation approach that explicitly models cross-type feature dependencies within a unified framework. Our method leverages both complete and incomplete instances to ensure accurate and consistent imputation in tabular data. Extensive experimental results demonstrate that the proposed approach achieves superior performance over existing techniques and significantly enhances downstream machine learning tasks, providing a robust solution for real-world systems with missing data.