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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.05017 |
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| _version_ | 1866909984886882304 |
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| author | Luo, Xiaopeng Tan, Zexi Wang, Zhuowei |
| author_facet | Luo, Xiaopeng Tan, Zexi Wang, Zhuowei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_05017 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HMVI: Unifying Heterogeneous Attributes with Natural Neighbors for Missing Value Inference Luo, Xiaopeng Tan, Zexi Wang, Zhuowei Machine Learning Artificial Intelligence 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. |
| title | HMVI: Unifying Heterogeneous Attributes with Natural Neighbors for Missing Value Inference |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2601.05017 |