Composition analysis and identification of ancient glass objects based on AdaBoost and CART classification tree

Abstract

Ancient glass is highly susceptible to weathering by the burial environment, and a series of chemical reactions will occur in the process, which leads to changes in the chemical composition of glass artifacts. In order to identify and classify the composition types of glass artifacts, this paper uses high potassium glass and lead-barium glass as target training models to derive CART stump (CART tree with only 2 layers) combinations as a way to analyze the classification laws. Then, we analyzed the sub-classification results of weathering, color, and ornamentation, and analyzed the classification rules according to their chemical composition, and came up with the classification method based on CART stumps. In order to identify the type of unknown types of glass, this paper uses an integrated learning algorithm model based on CART classification tree and AdaBoost to train a prediction model using all the samples, with the objective of artifact type, and then performs type prediction on the data. This study is important for the correct classification of glass types.