A Tabular Data Augmentation Framework Based on Error-Focused XAI-Supported Weighting Strategy: Application to Soil Liquefaction Classification

Abstract

In tabular liquefaction datasets, data augmentation plays a crucial role in enhancing the classification performance of machine learning models. In this study, an XAI-supported, error-focused, weighting-based data augmentation framework is proposed to improve CPT-based soil liquefaction classification in data-limited case-history settings by leveraging feedback from test misclassifications. First, it is hypothesized that test errors are non-random and that certain features contributed the most to misclassifications. Accordingly, a SHAP-based error-contribution score approach was developed to identify error-contributing features. The core of the proposed framework relies on assigning weights to error-contributing features. This targeted weighting was employed in two components: (i) clustering to select training samples for augmentation; and (ii) noise injection applied only in difficult-to-predict regions. To this end, test errors were combined with the training data, and weighted Fuzzy C-Means clustering was applied by assigning a weight of 1.5 to the distance metric in the error-contributing features. Clusters where test errors were concentrated were therefore defined as “difficult-to-predict regions”. In these clusters, noise was injected into the error-contributing features with 1.5× higher amplitude. This design directly integrated XAI-based error explanations into the data augmentation process, enabling targeted augmentation in difficult-to-predict regions. Consequently, the decision boundaries of the models became sharper, particularly in the error-contributing features. The Random Forest model achieved the highest improvement, with its F1 score increasing by 0.019. These findings demonstrate that the proposed framework enhances classification performance for tabular liquefaction data.