Classification and identification of glass artifacts based on high-dimensional clustering and XGBoost algorithm
DOI: 10.23977/acss.2022.060601 | Downloads: 25 | Views: 739
Author(s)
Yilin Zeng 1
Affiliation(s)
1 School of Economics &Management, Shanghai Maritime University, Shanghai, 201306, China
Corresponding Author
Yilin ZengABSTRACT
After the weathering of glass artifacts, a large number of environmental elements are exchanged with elements inside the glass artifacts. In this paper, based on the chemical composition of the artifact samples and other detection means, they are classified into two types: lead-barium glass and high-potassium glass. A sub-classification model based on high-dimensional clustering is introduced, and an identification model is established by optimizing the K-means algorithm. Finally, a feature classification model based on XGBoost algorithm is used to analyze the chemical composition of the unknown class of glass artifacts.
KEYWORDS
Integrated learning; high-dimensional clustering; XGBoost; glass classificationCITE THIS PAPER
Yilin Zeng, Classification and identification of glass artifacts based on high-dimensional clustering and XGBoost algorithm. Advances in Computer, Signals and Systems (2022) Vol. 6: 1-7. DOI: http://dx.doi.org/10.23977/acss.2022.060601.
REFERENCES
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[2] Lu Yue. Deep learning-based classification and detection of cell phone glass defects [D]. Zhengzhou University., 2019.
[3] Shi Youzhou, A kind of glass sorting storage rack. Anhui Province, Anhui Youtong Glass Co., 2019-04-19.
[4] Cheng Wei. Research on machine vision-based defect detection and classification system for glass fiber electronic fabric [D]. Xi'an University of Engineering, 2018.
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