Towards A Distribution Alignment Framework for Incomplete Data Classification

Abstract

Missing attribute values frequently affect data classification, reducing accuracy as most models rely on complete datasets. Imputing missing values is typically used to restore data completeness, which is essential for building models. The effectiveness of imputation significantly impacts the classification accuracy. Therefore, improving imputed values’ quality is crucial for better classification outcomes. Here, we introduce a new distribution alignment framework (DAF) to address classification issues with complete training data but incomplete test data. Initially, DAF imputes missing test data values using mean vectors from complete training data, minimizing the first-order distributional discrepancies. Next, it aligns the second-order statistical distributions, specifically covariance matrices, of both training and imputed test data to derive a feature transformation matrix. This matrix generates new feature representations for the incomplete test data. The classifier trained on the complete training data then classifies the imputed test data under this new feature representation. The experiments on several benchmark datasets show that DAF usually outperforms many advanced methods, achieving the higher classification performance.