Handling mislabeled data in fault diagnosis: A graph-assisted random forest approach
Authors:
- Shaozhi ChenSCShaozhi Chen
- Xiaopeng XiXXXiaopeng Xi
- Maiying ZhongMZMaiying Zhong
- Rui YangRYRui Yang
- Marcos E. OrchardMOMarcos E. Orchard
- 9 January 2026
- journal article
- Published by Elsevier in Neurocomputing
- Vol. 671
Abstract
No abstract availableFunding Information
- National Natural Science Foundation of China (62233012)
- Fondo Nacional de Desarrollo Científico y Tecnológico (1250036)
- Agencia Nacional de Investigación y Desarrollo
- Taishan Scholar Project of Shandong Province
- Taishan Scholar Foundation of Shandong Province
This publication has 30 references indexed in Scilit:
- SSPENet: Semi-supervised prototype enhancement network for rolling bearing fault diagnosis under limited labeled samplesAdvanced Engineering Informatics, 2024
- Bearing fault diagnosis via fusing small samples and training multi-state Siamese neural networksNeurocomputing, 2024
- Adversarial domain adaptation network with MixMatch for incipient fault diagnosis of PMSM under multiple working conditionsKnowledge-Based Systems, 2023
- A hybrid SVM and kernel function-based sparse representation classification for automated epilepsy detection in EEG signalsNeurocomputing, 2023
- Random forest feature selection for partial label learningNeurocomputing, 2023
- A novel momentum prototypical neural network to cross-domain fault diagnosis for rotating machinery subject to cold-startNeurocomputing, 2023
- Fine tuning attribute weighted naive BayesNeurocomputing, 2022
- Remaining useful life prediction for fractional degradation processes under varying modesThe Canadian Journal of Chemical Engineering, 2019
- Making risk minimization tolerant to label noiseNeurocomputing, 2015
- A general graph-based semi-supervised learning with novel class discoveryNeural Computing and Applications, 2009