A Homogeneous-Heterogeneous Ensemble of Classifiers.
Anh Vu LuongTrung Hieu VuPhuong Minh NguyenNang Van PhamJohn A. W. McCallAlan Wee-Chung LiewTien Thanh NguyenPublished in: ICONIP (5) (2020)
Keyphrases
- ensemble learning
- ensemble classifier
- classifier ensemble
- multiple classifiers
- ensemble pruning
- training data
- training set
- majority voting
- randomized trees
- individual classifiers
- final classification
- ensemble methods
- majority vote
- combining classifiers
- multiple classifier systems
- ensemble members
- ensemble classification
- classifier combination
- decision trees
- decision tree classifiers
- feature selection
- support vector
- trained classifiers
- imbalanced data
- accurate classifiers
- class label noise
- random forests
- base classifiers
- weak classifiers
- weak learners
- mining concept drifting data streams
- concept drifting data streams
- bias variance decomposition
- random forest
- training samples
- test set
- one class support vector machines
- learning algorithm
- diversity measures
- naive bayes
- weighted voting
- classifier fusion
- binary classification problems
- logistic regression
- classification systems
- pruning method
- linear support vector machines
- training examples
- classification algorithm
- machine learning methods
- linear classifiers
- generalization ability
- neural network
- rule induction algorithm
- data sets
- classification models
- feature subset
- support vector machine
- publicly available data sets
- benchmark datasets