Novel SVM based SMOTE integrated LPP Dimensionality Reduction Method for Imbalanced Samples Fault Diagnosis.
Yiguang ZhangZhongqing JiaHailong GeJun WangPublished in: CAA SAFEPROCESS (2021)
Keyphrases
- fault diagnosis
- dimensionality reduction
- dimensionality reduction methods
- minority class
- class imbalance
- support vector machine
- class distribution
- imbalanced data
- principal component analysis
- imbalanced data sets
- data sets
- high dimensionality
- discriminant analysis
- discriminant projection
- expert systems
- knn
- neural network
- class imbalanced
- pattern recognition
- feature selection
- monitoring and fault diagnosis
- fault detection
- operating conditions
- sampling methods
- locality preserving projections
- high dimensional data
- fisher discriminant analysis
- high dimensional
- active learning
- principal components analysis
- feature extraction
- training samples
- preprocessing step
- fuzzy logic
- data points
- manifold learning
- random projections
- chemical process
- feature space
- support vector
- training data
- principal components
- low dimensional
- support vector machine svm
- training set
- cost sensitive
- unsupervised learning
- face recognition
- original data
- image processing
- machine learning
- dimension reduction
- feature selection algorithms
- multi sensor information fusion