A confident learning-based support vector machine for robust ground classification in noisy label environments

•A confident learning-based support vector machine is proposed for robust ground classification.•The model recognizes and removes noisy labels by ranking optimized confidence values.•The model still exhibits nearly 90% accuracy and 0.8 credibility under a noise ratio of up to 35%.•A confidence crite...

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Bibliographic Details

Published in:	Tunnelling and underground space technology Vol. 155; p. 106128
Main Authors:	Zhang, Xin-Yue, Zhang, Xiao-Ping, Yu, Hong-Gan, Liu, Quan-Sheng
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.01.2025
Subjects:	Confident learning Ground classification Model robustness Noisy label Support vector machine Model robustness Noisy label Confident learning Support vector machine Ground classification
ISSN:	0886-7798
Online Access:	Get full text
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