Intelligent Feature Extraction for Ensemble of Classifiers.
Paulo Vinicius Wolski RadtkeRobert SabourinTony WongPublished in: ICDAR (2005)
Keyphrases
- feature extraction
- ensemble learning
- feature selection
- ensemble classifier
- multiple classifiers
- classifier ensemble
- extracted features
- training data
- ensemble pruning
- feature extractors
- feature set
- training set
- majority voting
- randomized trees
- individual classifiers
- decision tree classifiers
- combining classifiers
- mining concept drifting data streams
- neural classifier
- ensemble methods
- concept drifting data streams
- final classification
- texture features
- multiple classifier systems
- face recognition
- weighted voting
- pattern classification
- random forest
- support vector
- accurate classifiers
- weak learners
- ensemble classification
- feature ranking
- preprocessing
- weak classifiers
- trained classifiers
- generalization ability
- training samples
- rule induction algorithm
- publicly available data sets
- diversity measures
- learning algorithm
- imbalanced data
- image processing
- image classification
- svm classifier
- classification algorithm
- classifier combination
- support vector machine svm
- class label noise
- neural network
- feature space
- principal component analysis
- pruning algorithm
- majority vote
- machine learning methods
- linear discriminant analysis
- classification models
- base classifiers
- combining multiple
- knn classifier
- machine learning algorithms
- random forests
- binary classification problems
- feature vectors
- one class support vector machines
- decision trees
- bias variance decomposition