Learning on Missing Tabular Data: Attention with Self-Supervision, Not Imputation, is All You Need

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Abstract
Learning from data with missing values is a common challenge in real-world applications. Existing approaches for handling data incompleteness often involve imputation, which can introduce errors that propagate into downstream tasks or impose assumptions that limit the support for heterogeneous feature types. To address these issues, we propose Missing Feature Attention Network ( MFAN ), an end-to-end label prediction model that directly consumes incomplete data without requiring imputation. MFAN flexibly accommodates both continuous and categorical features through learnable embeddings, and leverages a transformer encoder with self-attention to capture the correlation among features as well as the correlation between features and missingness . This attention-based mechanism allows missing features to benefit from relationships learned among observed features, leading to enhanced hidden representations and robust prediction performance. Additionally, we introduce auxiliary self-supervised pre-training tasks that further guide the attention mechanism in modeling missingness. Experimental results on eight regression and seven classification datasets demonstrate MFAN ’s superiority over state-of-the-art end-to-end methods and imputation-based approaches. Comprehensive ablation studies confirm the effectiveness of each MFAN component, underscoring the importance of explicitly modeling correlations among observed and missing features.

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