This paper was to construct ensemble feature selection and random forest for the identification of ancient glass products via integrating the machine learning algorithm into the identification and analysis of ancient glass products and taking accuracy rate and AUC as the measurement indexes of classification performance. The results of different feature selection methods were analyzed, and the important chemical components were selected. The selected important features with random forest, k-nearest neighbor learning and naive Bayesian were investigated. The results show that lead oxide, barium oxide, and potassium oxide have an impact on the weathering of the glass surface by using ensemble feature selection. In high potassium glass, three components are closely related, the accuracy of classification by the random forest based on the k-fold cross validation for selected important features is great, and the model is stable. This method can provide a theoretical reference for the composition analysis and category identification of ancient glass products, and other glasses.

machine learning  /  random forest  /  k-nearest neighbor  /  classification algorithm