An Empirical Evaluation of Bagging and Boosting.
Richard MaclinDavid W. OpitzPublished in: AAAI/IAAI (1997)
Keyphrases
- ensemble methods
- ensemble learning
- base classifiers
- ensemble classification
- randomized trees
- gradient boosting
- weak classifiers
- majority voting
- decision tree ensembles
- ensemble classifier
- base learners
- random forests
- decision stumps
- prediction accuracy
- rotation forest
- decision trees
- random forest
- variance reduction
- machine learning
- weak learners
- benchmark datasets
- tree induction
- imbalanced data
- weighted voting
- generalization ability
- machine learning methods
- boosting algorithms
- learning algorithm
- classifier ensemble
- meta learning
- multi class
- training set
- training data
- data sets
- classification error
- concept drift
- naive bayes
- training samples
- neural network ensembles
- classifier combination
- classification models
- voting methods
- negative correlation learning