Fast Iterative and Task-Specific Imputation with Online Learning

• URL: http://arxiv.org/abs/2501.13786v1
• Date: Thu, 23 Jan 2025 16:04:18 GMT
• Title: Fast Iterative and Task-Specific Imputation with Online Learning
• Authors: Rahul Bordoloi, Clémence Réda, Saptarshi Bej,
• Abstract summary: We propose an imputation approach named F3I based on the iterative improvement of a K-nearest neighbor imputation. We provide a theoretical analysis of the imputation quality by F3I for several types of missing mechanisms. We also demonstrate the performance of F3I on both synthetic data sets and real-life drug repurposing and handwritten-digit recognition data.
• Score: 2.7855886538423187
• License:
• Abstract: Missing feature values are a significant hurdle for downstream machine-learning tasks such as classification and regression. However, they are pervasive in multiple real-life use cases, for instance, in drug discovery research. Moreover, imputation methods might be time-consuming and offer few guarantees on the imputation quality, especially for not-missing-at-random mechanisms. We propose an imputation approach named F3I based on the iterative improvement of a K-nearest neighbor imputation that learns the weights for each neighbor of a data point, optimizing for the most likely distribution of points over data points. This algorithm can also be jointly trained with a downstream task on the imputed values. We provide a theoretical analysis of the imputation quality by F3I for several types of missing mechanisms. We also demonstrate the performance of F3I on both synthetic data sets and real-life drug repurposing and handwritten-digit recognition data.

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