Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning

Chen, J, Huang, H, Cohn, AG orcid.org/0000-0002-7652-8907 et al. (2 more authors) (2022) Machine learning-based classification of rock discontinuity trace: SMOTE oversampling integrated with GBT ensemble learning. International Journal of Mining Science and Technology, 32 (2). pp. 309-322. ISSN 2095-2686

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: © 2021 Published by Elsevier B.V. on behalf of China University of Mining & Technology. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Generalization ability; Gradient boosting tree; Machine learning; Rock discontinuity trace; Tunnel face
Dates:
  • Accepted: 25 August 2021
  • Published (online): 13 September 2021
  • Published: March 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 16 Nov 2021 16:11
Last Modified: 17 Jun 2022 14:01
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.ijmst.2021.08.004

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