Skip to main content
Have a personal or library account? Click to login
Improved Random Forest Fault Diagnosis Model Based on Fault Ratio Cover

Improved Random Forest Fault Diagnosis Model Based on Fault Ratio

International Journal of Advanced Network, Monitoring and Controls
Volume 7 (2022): Issue 2 (January 2022)
By: Ziwei Ding and  Shunyuan Huang  
Open Access
|May 2023
Authors

Authors

Ziwei Ding

146377997@qq.com

School of Computer Science and Engineering, Xi'an Technological University, Xi'an, China
State and Provincial Joint Engineering, Lab of Advanced Network, Monitoring and Control, Xi'an, China

Shunyuan Huang

sylvianoemie@sina.com

School of Computer Science and Engineering, Xi'an Technological University, Xi'an, China
State and Provincial Joint Engineering, Lab of Advanced Network, Monitoring and Control, Xi'an, China
DOI: https://doi.org/10.2478/ijanmc-2022-0019 | Journal eISSN: 2470-8038
Journal RSS Feed
Language: English
Page range: 85 - 91
Published on: May 26, 2023
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Complex Equipment,
Fault Diagnosis,
Random Forest,
Unbalanced Data
Related subjects:
Computer sciences,
Computer sciences, other

© 2023 Ziwei Ding, Shunyuan Huang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 7 (2022): Issue 2 (January 2022)
Previous article
Next article