Cited by 60Laser-induced spectroscopy and plasmaSpectroscopy and Chemometric AnalysesSpectroscopy and Laser Applications

Classification of steel samples by laser-induced breakdown spectroscopy and random forest

Zhang, Tianlong, Xia, Donghui, Tang, Hongsheng, Yang, Xiaofeng, Li, Hua·2016·Chemometrics and Intelligent Laboratory Systems

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Registry

Crossref · 10.1016/j.chemolab.2016.07.001

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  • Decision trees, random forests, and relevance vector machines (RVMs) have achieved good effects under supervised learning for flat steel surface defect classification.

    “In addition to the above classifiers, some other classifiers have also achieved good effects under supervised learning for the task of flat steel surface defect classification, such as decision trees, random forests, and relevance vector machines (RVMs).”

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Authors
Zhang, T., Xia, D., Tang, H., Yang, X., & Li, H.
Affiliations
  • Northwest University (CN)
  • Shaanxi University of Technology (CN)
  • Xi'an Shiyou University (CN)
Corresponding author
Li, Hua
Venue
Chemometrics and Intelligent Laboratory Systems
Publisher
Elsevier BV
Published
2016-10
Volume
157
Pages
196-201
ISSN
0169-7439 (ISSN-L)
OpenAlex
W2466636344
Cited by
60
References
31
Citations by year
2026: 1 · 2025: 6 · 2024: 5 · 2023: 4 · 2022: 4 · 2021: 4 · 2020: 5 · 2019: 7 · 2018: 12 · 2017: 11 · 2013: 1
FWCI
8.63
Type
journal article
Language
en
Open access
closed
Locations
Chemometrics and Intelligent Laboratory Systems (publishedVersion)
Funders
  • National Major Scientific Instruments and Equipment Development Projects of China (2011YQ030113)
  • National Natural Science Foundation of China (21175106, 21375105)
  • Research Fund for the Doctoral Program of Higher Education of China (20126101110019)
Topics
  • Physical Sciences › Engineering › Mechanics of Materials › Laser-induced spectroscopy and plasma
  • Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
  • Physical Sciences › Chemistry › Spectroscopy › Spectroscopy and Laser Applications
Keywords
laser-induced breakdown spectroscopy, random forest, steel composition, real-time classification

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