Dynamic ensemble selection based improved random forests for fault classification in industrial processes.
Junhua ZhengYue LiuZhiqiang GePublished in: IFAC J. Syst. Control. (2022)
Keyphrases
- random forests
- industrial processes
- fault detection and isolation
- fault detection
- decision trees
- tree ensembles
- machine learning algorithms
- random forest
- regression forests
- ensemble methods
- ensemble selection
- logistic regression
- classification accuracy
- fault diagnosis
- benchmark datasets
- text classification
- feature set
- support vector machine svm
- feature selection
- machine learning
- feature extraction
- machine learning methods
- expert systems
- artificial neural networks
- quality improvement
- feature vectors
- classification models
- training set
- support vector machine
- supervised learning
- dimension reduction
- image classification
- metaheuristic
- unsupervised learning