Using boosting to prune bagging ensembles.
Gonzalo Martínez-MuñozAlberto SuárezPublished in: Pattern Recognit. Lett. (2007)
Keyphrases
- ensemble methods
- ensemble learning
- base classifiers
- decision tree ensembles
- rotation forest
- random forests
- base learners
- decision stumps
- negative correlation learning
- prediction accuracy
- ensemble classifier
- decision trees
- weak learners
- benchmark datasets
- random forest
- generalization ability
- ensemble classification
- ensemble members
- weighted voting
- majority voting
- machine learning methods
- imbalanced data
- randomized trees
- search space
- boosting algorithms
- classifier ensemble
- meta learning
- ensemble selection
- multi class
- error function
- learning machines
- training samples
- individual classifiers
- weak classifiers
- random subspace
- subspace methods
- classifier combination
- multiple classifier systems
- class distribution
- classification error
- concept drift
- classification algorithm
- naive bayes
- model selection
- training set