Study on Chemical Composition Subdivision of Glass Relics Based on Random Forest

DOI

10.2991/978-94-6463-238-5_85How to use a DOI?

Keywords

Random forest; decision tree; classification law; feature selection; subclass classification

Abstract

Random Forests is a statistical learning theory-based combinatorial classifier, an integrated learning algorithm based on decision trees, with high prediction accuracy, which combines bootstrap resampling method and decision tree algorithm, the essence of the algorithm is to construct a set of tree classifiers h k (x), k1, and then use the set to classify and predict by voting for classification and prediction. Ancient glass was made locally by absorbing its technology, but the chemical composition was different. In this paper, we used the random forest algorithm to draw a rose diagram of the importance of chemical components affecting the classification pattern of glass, combined the top five chemical components in the rose diagram, and used a hierarchical clustering algorithm to classify them according to their average content, and gave the classification results. The study shows that the random forest algorithm has a good discriminative effect on the classification study of chemical components of glass classification laws, and provides a fast and feasible method for glass classification laws.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jinlong Li
AU  - Jinde Li
AU  - Kejing Chen
PY  - 2023
DA  - 2023/09/26
TI  - Study on Chemical Composition Subdivision of Glass Relics Based on Random Forest
BT  - Proceedings of the 2023 4th International Conference on Big Data and Informatization Education (ICBDIE 2023)
PB  - Atlantis Press
SP  - 647
EP  - 653
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-238-5_85
DO  - 10.2991/978-94-6463-238-5_85
ID  - Li2023
ER  -