A model for identifying glass artifacts based on SOM clustering analysis and random forest algorithm

Abstract

With the deepening of human awareness of heritage conservation today, the category identification of excavated glass artifacts is particularly important. This paper draws on data related to the chemical composition of some glass artifacts. The data were analyzed by Mann-Whitney U analysis, SOM cluster analysis, and random forest algorithm, and a model was developed to accurately identify the categories of glass artifacts based on their chemical composition. The results show that the three chemical components of potassium oxide, barium oxide, and lead oxide have the greatest influence on the weathering and corrosion of glass artifacts through Mann-Whitney U analysis; the SOM cluster analysis model shows that the glass artifacts with high potassium can be divided into two subclasses, and the glass artifacts with lead and barium can be divided into three subclasses. Finally, we combined the results of the existing analysis and used the random forest algorithm to establish a model for accurate identification of glass artifacts based on their chemical composition. The sensitivity test shows that the model has high robustness and accuracy. This method will play an important role in the accurate identification of glass artifacts of unknown categories in the future.