Pruning Bagging Ensembles with Metalearning.
Fábio PintoCarlos SoaresJoão Mendes-MoreiraPublished in: MCS (2015)
Keyphrases
- ensemble methods
- meta learning
- ensemble learning
- ensemble selection
- base classifiers
- imbalanced data
- base learners
- ensemble members
- neural network ensembles
- random forests
- decision trees
- decision tree ensembles
- tree ensembles
- ensemble classifier
- random forest
- prediction accuracy
- search space
- classifier ensemble
- rotation forest
- multi class
- variable selection
- learning machines
- random subspace
- selection strategy
- machine learning methods
- learning tasks
- majority voting
- data mining problems
- naive bayes
- decision stumps
- model selection
- subspace methods
- negative correlation learning
- machine learning
- feature selection
- machine learning algorithms
- benchmark datasets
- inductive learning
- parameter settings
- data mining
- linear regression
- weighted voting
- pruning method
- pruning algorithm
- class imbalance
- cost sensitive
- generalization ability
- class labels
- generalization error
- class distribution
- regression trees