Weighed Aging Ensemble of Heterogenous Classifiers for Incremental Drift Classification.
Michal WozniakPiotr CalPublished in: MICAI (2) (2013)
Keyphrases
- ensemble classifier
- final classification
- training set
- multiple classifiers
- majority voting
- classifier ensemble
- classification systems
- multiple classifier systems
- decision trees
- feature selection
- concept drifting data streams
- classification algorithm
- classification accuracy
- classification models
- combining classifiers
- individual classifiers
- accurate classifiers
- concept drift
- ensemble learning
- class labels
- classification method
- support vector
- training data
- classification rate
- supervised classification
- classifier combination
- incremental learning
- decision tree classifiers
- feature ranking
- machine learning methods
- ensemble classification
- feature set
- training samples
- image classification
- base classifiers
- binary classification problems
- classification process
- binary classifiers
- randomized trees
- accurate classification
- classification decisions
- nearest neighbor classifiers
- k nearest neighbour
- machine learning
- support vector machine
- svm classifier
- multiclass classification
- combination of multiple classifiers
- class probabilities
- decision boundary
- random forest
- text classification
- probabilistic classifiers
- ensemble pruning
- feature vectors
- data sources
- multi class
- neural network
- ensemble methods
- generalization ability
- imbalanced data
- weak classifiers
- feature extraction
- supervised learning
- feature space
- multi category
- naive bayes
- nearest neighbour