A Heuristically Perturbation of Dataset to Achieve a Diverse Ensemble of Classifiers.
Hamid ParvinSajad ParvinZahra RezaeiMoslem MohamadiPublished in: MCPR (2012)
Keyphrases
- training data
- training set
- multiple classifiers
- ensemble learning
- classifier ensemble
- ensemble classifier
- feature selection
- feature set
- majority voting
- ensemble members
- majority vote
- class label noise
- ensemble pruning
- ensemble classification
- imbalanced data
- multiple classifier systems
- data sets
- mining concept drifting data streams
- neural network
- decision trees
- support vector
- combining classifiers
- weighted voting
- final classification
- decision tree classifiers
- individual classifiers
- base classifiers
- classifier combination
- support vector machine
- ensemble methods
- naive bayes
- random forests
- diversity measures
- concept drifting data streams
- accurate classifiers
- training samples
- weak learners
- benchmark datasets
- svm classifier
- learning algorithm
- classification algorithm
- generalization ability