Classification via ensembles of basic thresholding classifiers.
Mehmet Altan ToksözIlkay UlusoyPublished in: IET Comput. Vis. (2016)
Keyphrases
- decision trees
- classification systems
- multiple classifier systems
- support vector
- classification algorithm
- classification method
- classifier ensemble
- classification models
- ensemble classifier
- supervised classification
- feature selection
- classification rate
- multiple classifiers
- classifier combination
- rule based classifier
- probabilistic classifiers
- combining classifiers
- class labels
- k nearest neighbour
- classification process
- feature set
- improves the classification accuracy
- optimum path forest
- classification procedure
- decision boundary
- classification accuracy
- ensemble learning
- machine learning algorithms
- naive bayes
- decision tree classifiers
- accurate classification
- machine learning methods
- nearest neighbor classifier
- support vector machine
- training set
- binary classifiers
- majority voting
- supervised learning
- svm classifier
- generalization ability
- ensemble methods
- feature vectors
- classification decisions
- multi category
- pattern recognition
- feature extraction
- text classification
- knn
- weighted voting
- accurate classifiers
- image segmentation
- individual classifiers
- machine learning
- feature space
- base learners
- fold cross validation
- multi class
- class distribution
- imbalanced data
- feature subset
- classifier fusion
- random forest
- feature ranking
- gray level
- decision stumps
- training samples
- image classification
- nearest neighbour