Imputation is Not Required: Incremental Feature Attention Learning of Tabular Data with Missing Values

Tabular data sets with varying missing values are prepared for machine learning using an arbitrary imputation strategy. Synthetic values generated by imputation models often raise concerns about computational complexity, data quality, and data-driven outcomes. To address these concerns, this article proposes a no-imputation incremental attention learning (NIAL) method for tabular data. A pair of attention masks is derived and retrofitted to a transformer to directly streamline tabular data without imputing or initializing missing values. The proposed method incrementally learns partitions of overlapping and fixed-size feature sets to enhance the efficiency and performance of the transformer. The average classification performance rank order across 15 diverse tabular data sets highlights the superiority of NIAL over 11 state-of-the-art learning methods with or without missing value imputations. Further experiments substantiate the robustness of NIAL against varying missing value types and rates compared to methods involving missing value imputation. Our analysis reveals that a feature partition size of half the original feature space is, both computationally and in terms of accuracy, the best choice for the proposed incremental learning. The proposed method is one of the first solutions to enable deep attention learning of tabular data without requiring missing-value imputation.