A new classification method of ancient Chinese ceramics based on machine learning and component analysis

Abstract

Ancient Chinese celadon is sought after all over the world for practical and artistic values. The study of ancient celadon is of great significance for understanding the cultural exchange, of which the classification of ancient celadon is an important part. The goal of this work was to establish a reliable celadon classification model based on EDXRF, machine learning algorithm and Mahalanobis distance. The data set for training machine learning models is constructed of 12 components in the ceramic body and glaze, which are measured by EDXRF. Comparing the predicted results of four machine learning models, the Random forest algorithm performed best on all evaluation indicators. Therefore, the Random forest was the most suitable algorithm for celadon classification with an average accuracy of 96.41% and a Kappa coefficient of 0.985. The contents of the chemical compositions of the sample were determined to be within the corresponding composition ranges of the predicted category. The chemical compositions with greater influence in identifying the categories of ancient ceramics in Random forest were chosen as the characteristic parameters. The general rules of the Mahalanobis distance from the sample to the category center were summarized and used to describe the similarity between the sample and the predicted category. The celadon classification model established by combining these two methods can make a more specific and accurate prediction. The celadon classification model was also adopted to predict the categories of samples excavated from the Jizhou kiln and Chuzhou site. The excellent prediction capability of the model was verified by comparing results with the corresponding background information of samples.