Explainable machine learning-based classification of traditional Korean ceramics using XRF chemical composition data

This study presents an explainable machine learning approach for classifying traditional Korean ceramics, including celadon, buncheong, and white porcelain, based on X-ray fluorescence chemical composition data. A curated dataset of 624 samples was analyzed using six machine learning algorithms: principal component analysis-linear discriminant analysis, decision tree, random forest, extreme gradient boosting, k-nearest neighbors, and support vector machine. Among them, tree-based random forest and extreme gradient boosting models achieved the highest classification accuracy of 95.8%. While white porcelain was accurately identified across all models, celadon and buncheong showed partial misclassification due to overlapping chemical characteristics. Model interpretability was enhanced using Shapley additive explanations, which identified Fe2O3 and TiO2 as the most influential components for type differentiation, consistent with established ceramic coloration mechanisms. These results demonstrate the effectiveness of explainable machine learning for chemical-based ceramic classification and provide a quantitative framework that complements traditional typological approaches in cultural heritage research.