DeepIFSA: Deep Imputation of Missing Values Using Feature and Sample Attention

2025-01-19

AI Summary

Existing methods struggle with robust imputation of real-world tabular data featuring high missingness rates (10%–50%) and non-ignorable, non-random missing patterns. Method: We propose the first deep imputation framework jointly modeling feature-wise and sample-wise attention. It innovatively integrates multi-head attention, CutMix-driven contrastive learning, and a missing-pattern-adaptive recommendation mechanism to substantially mitigate estimation uncertainty. The framework is trained end-to-end to optimize reconstruction fidelity without assuming any specific missing-data mechanism. Contribution/Results: Our method consistently outperforms nine state-of-the-art imputation approaches across 12 benchmark datasets. On real-world electronic health record data, it achieves the highest downstream classification accuracy. This work establishes a new paradigm for reconstructing tabular data under high-missingness and non-ignorable missingness scenarios.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Computer Vision: Multi-modal Vision
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Missing values of varying patterns and rates in real-world tabular data pose a significant challenge in developing reliable data-driven models. Existing missing value imputation methods use statistical and traditional machine learning, which are ineffective when the missing rate is high and not at random. This paper explores row and column attention in tabular data to address the shortcomings of existing methods by introducing a new method for imputing missing values. The method combines between-feature and between-sample attention learning in a deep data reconstruction framework. The proposed data reconstruction uses CutMix data augmentation within a contrastive learning framework to improve the uncertainty of missing value estimation. The performance and generalizability of trained imputation models are evaluated on set-aside test data folds with missing values. The proposed joint attention learning outperforms nine state-of-the-art imputation methods across several missing value types and rates (10%-50%) on twelve data sets. Real electronic health records data with missing values yield the best classification accuracy when imputed using the proposed attention learning compared to other statistical, machine learning, and deep imputation methods. This paper highlights the heterogeneity of tabular data sets to recommend imputation methods based on missing value types and data characteristics.

Problem

• Missing Values
• Table Data
• Machine Learning

Innovation

• DeepIFSA
• CutMix Technique
• Missing Data Imputation