FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification.
Sebastián MaldonadoCarla VairettiAlberto FernándezFrancisco HerreraPublished in: Pattern Recognit. (2022)
Keyphrases
- class imbalance
- minority class
- imbalanced data sets
- imbalanced datasets
- class distribution
- imbalanced data
- class imbalanced
- cost sensitive
- majority class
- active learning
- feature vectors
- cost sensitive learning
- high dimensionality
- classification accuracy
- feature set
- decision boundary
- training dataset
- binary classification problems
- pattern classification
- pattern recognition
- rare events
- highly skewed
- machine learning
- decision trees
- classification models
- image features
- support vector machine
- training set
- feature selection
- classification algorithm
- class labels
- feature values
- sampling methods
- feature space
- text classification
- data sets
- feature extraction
- training samples
- k nearest neighbour
- benchmark data sets
- roc curve