Cited by 21Industrial Vision Systems and Defect DetectionMachine Learning and ELMData Mining and Machine Learning Applications

Multi-class Classification Methods of Enhanced LS-TWSVM for Strip Steel Surface Defects

Chu, Mao-xiang, Wang, An-na, Gong, Rong-fen, Sha, Mo·2014·Journal of Iron and Steel Research International

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Crossref · 10.1016/s1006-706x(14)60027-3

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10 of 10 references verified0 citation errors · 0 not checkable

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Authors
Chu, M.-X., Wang, A.-N., Gong, R.-F., & Sha, M.
Affiliations
Northeastern University (CN)
Venue
Journal of Iron and Steel Research International
Published
2014-02
Volume
21
Issue
2
Pages
174-180
ISSN
  • 2210-3988 (ISSN-L)
  • 1006-706X
OpenAlex
W1963937997
Cited by
21
References
14
Citations by year
2025: 2 · 2024: 2 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 1
FWCI
5.64
Type
journal article
Language
en
Open access
closed
Locations
Journal of Iron and Steel Research International (publishedVersion)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
  • Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
Keywords
multi-class classification, least squares twin support vector machine, binary trees, weight parameter, class imbalance, large-scale data

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