Glass component classification model based on decision tree and cluster analysis

Abstract

In order to investigate the classification laws of the two types, three machine learning models (decision tree, SVM) were constructed in this paper, and their classification accuracy was 96%, which met the practical requirements. Subsequently, a K-means algorithm was constructed to classify the subclasses, and the high potassium and lead-barium glasses were divided into three subclasses. By descriptive statistics of the differences between the subclasses, the results showed that there existed a better differentiation of the divided subclasses in terms of multiple chemical compositions as well as ornamentation and color, which verified its reasonableness. By setting a perturbation factor (a normally distributed sequence with a mean of 0 and a standard deviation of 3) to test the sensitivity of the classification results, the model classification results did not change after several repetitions of the experiment and showed good robustness.