Classification and prediction of the chemical composition of glass based on grey and principal component Logistic regression

Hanyi Zhang; Yuwen Luo; Yang Chen

doi:10.54097/hset.v40i.6567

Authors

Hanyi Zhang
Yuwen Luo
Yang Chen

DOI:

https://doi.org/10.54097/hset.v40i.6567

Keywords:

GM (1,1) Grey Prediction, Principal Component Logistic Regression Model, Classification of Ancient Glass, Glass Weathering.

Abstract

As a momentous witness of the ancient Silk Road trade, glass’s variety identification and measurement or even prediction of its chemical constituent content are significantly important for people to have a systematic understanding of it. However, glass weathering tends to occur because of the ambient conditions of the place where it is buried, which may bring some difficulties to the classification and content prediction. Based on the data provided in problem C of the National Undergraduate Mathematical Modeling Contest in 2022, the GM (1,1) grey prediction model has first constructed so that the content ranges of various pivotal components are achieved. Then several glass samples whose category is unknown could be classified based on a principal component logistic regression model of the chemical composition’s content.

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