Bagging and Boosting with Dynamic Integration of Classifiers.
Alexey TsymbalSeppo PuuronenPublished in: PKDD (2000)
Keyphrases
- dynamic integration
- weighted voting
- random forests
- majority voting
- randomized trees
- ensemble learning
- ensemble methods
- base classifiers
- ensemble classifier
- accurate classifiers
- base learners
- decision trees
- classifier ensemble
- random forest
- weak classifiers
- decision stumps
- fusion methods
- topology preserving
- ensemble classification
- individual classifiers
- data fusion
- machine learning algorithms
- generalization ability
- naive bayes
- weak learners
- logistic regression
- voting method
- prediction accuracy
- binary classification
- benchmark datasets
- training set
- learning algorithm
- meta learning
- knn
- multi class
- classifier combination
- classification error
- boosting algorithms
- multiple classifier systems
- cost sensitive