Research on classification of weathered glass based on logistic regression

Chi Zhang; Yuewen Li; Hao Zheng

doi:10.1117/12.2685513

15 August 2023 Research on classification of weathered glass based on logistic regression

Chi Zhang, Yuewen Li, Hao Zheng

Proceedings Volume 12719, Second International Conference on Electronic Information Technology (EIT 2023); 127191C (2023) https://doi.org/10.1117/12.2685513
Event: Second International Conference on Electronic Information Technology (EIT 2023), 2023, Wuhan, China

Abstract

In order to classify glass artifacts into different categories based on two characteristics: the chemical composition of the sampling points and whether they are weathered or not, this paper is divided into two parts. In the first part, the entire dataset is divided using data preprocessing, and a logistic regression model is constructed for binary classification. The optimal parameters of the model are estimated using maximum likelihood estimation and gradient descent algorithm. The aim is to explore the classification patterns of the two types of glass artifacts. In the second part, unsupervised learning k-means algorithm is used for clustering. Indicators are selected based on mean square error and confidence level. The same model as in the first part is used to test the effectiveness of sub-classification. The results show that glass artifacts can be divided into two main categories: high-potassium glass and lead-barium glass, and further subdivided into 14 subcategories.

Citation Download Citation

Chi Zhang, Yuewen Li, and Hao Zheng "Research on classification of weathered glass based on logistic regression", Proc. SPIE 12719, Second International Conference on Electronic Information Technology (EIT 2023), 127191C (15 August 2023); https://doi.org/10.1117/12.2685513

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