EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling.
Mikel GalarAlberto FernándezEdurne Barrenechea TartasFrancisco HerreraPublished in: Pattern Recognit. (2013)
Keyphrases
- highly imbalanced
- imbalanced data
- class distribution
- class imbalance
- data sets
- cost sensitive
- base classifiers
- ensemble methods
- support vector machine
- cost sensitive learning
- random forest
- training set
- sampling methods
- database
- active learning
- feature selection
- ensemble learning
- decision trees
- training data
- misclassification costs
- test set
- high dimensionality
- concept drift
- multi class
- random forests
- classification models
- ensemble classifier
- machine learning methods
- text categorization
- training samples
- unlabeled data
- high dimensional