Oversampling the Minority Class in the Feature Space.
María Pérez-OrtizPedro Antonio GutiérrezPeter TiñoCésar Hervás-MartínezPublished in: IEEE Trans. Neural Networks Learn. Syst. (2016)
Keyphrases
- minority class
- feature space
- class imbalance
- support vector machine
- original data
- majority class
- training set
- high dimensionality
- class distribution
- decision boundary
- classification error
- training samples
- imbalanced data
- feature selection
- imbalanced datasets
- feature vectors
- nearest neighbour
- cost sensitive learning
- input space
- high dimensional
- hyperplane
- kernel function
- data points
- active learning
- feature extraction
- principal component analysis
- sampling methods
- classification accuracy
- training dataset
- dimensionality reduction
- training data
- cost sensitive
- kernel methods
- multi class
- feature set
- base classifiers
- face recognition
- ensemble learning
- classification algorithm
- feature selection algorithms
- concept drift
- test data
- test set
- low dimensional
- learning algorithm