DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive Framework

• URL: http://arxiv.org/abs/2501.10910v2
• Date: Wed, 05 Feb 2025 18:29:09 GMT
• Title: DeepIFSAC: Deep Imputation of Missing Values Using Feature and Sample Attention within Contrastive Framework
• Authors: Ibna Kowsar, Shourav B. Rabbani, Yina Hou, Manar D. Samad,
• Abstract summary: Existing missing value imputation methods are ineffective when the missing rate is high and not at random. We develop a novel framework to reconstruct missing values using row and column attention as between-feature and between-sample attention. We evaluate the quality of imputed data using real-world electronic health records with missing values.
• Score: 0.0
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• 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 and are ineffective when the missing rate is high and not at random. This paper explores row and column attention in tabular data as between-feature and between-sample attention in a novel framework to reconstruct missing values. The proposed method uses the 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 framework outperforms nine state-of-the-art imputation methods across several missing value types and rates (10\%-50\%) on a diverse selection of twelve tabular data sets. We evaluate the quality of imputed data using real-world electronic health records with missing values, demonstrating our proposed framework's superiority to state-of-the-art 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.

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