The Boosting and Bootstrap Ensemble for Classifiers Based on Weak Rough Inclusions.
Piotr ArtiemjewPublished in: RSFDGrC (2015)
Keyphrases
- ensemble learning
- ensemble classifier
- weak learners
- ensemble methods
- randomized trees
- weak classifiers
- majority voting
- ensemble classification
- bootstrap sampling
- base classifiers
- strong classifier
- multiple classifier systems
- weighted voting
- classifier ensemble
- base learners
- multiple classifiers
- combining classifiers
- decision trees
- individual classifiers
- feature selection
- generalization ability
- random forests
- decision stumps
- boosting algorithms
- random forest
- rough sets
- ensemble members
- adaboost algorithm
- training samples
- final classification
- accurate classifiers
- learning algorithm
- naive bayes
- concept drift
- machine learning algorithms
- prediction accuracy
- training set
- machine learning methods
- support vector machine
- boosting framework
- ensemble pruning
- rough mereology
- training data
- benchmark datasets
- cross validation
- linear classifiers
- improving classification accuracy
- multi class classification
- fusion method
- generalization error
- class labels
- feature subspace
- approximate reasoning
- feature ranking
- binary classification
- multi class
- support vector
- negative correlation learning