Glass Classification and Identification based on Decision Tree and Random Forest Classification Models

Zhipeng Zhang; Tong Wang; Xinpeng Wang

doi:10.54097/hset.v39i.6571

Authors

Zhipeng Zhang
Tong Wang
Xinpeng Wang

DOI:

https://doi.org/10.54097/hset.v39i.6571

Keywords:

Decision Tree Classification; Machine Learning; Random Forest Classification; Glass Artifacts.

Abstract

Glass artifacts are weathered due to age and improper preservation, etc. This paper analyzes the classification laws of high potassium glass and lead-barium glass based on the data; for each category, suitable chemical compositions are selected to classify them into subcategories, and firstly, a decision tree classification model is used to analyze the classification laws of the two categories of glass by substituting the data set for machine learning, and specific classification methods and classification results are given. And the chemical composition of the unknown category of glass artifacts is analyzed, and the random forest classification model is used to predict the training set and test set by.

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