Ensemble of complete P-partite graph classifiers for non-stationary environments.
João R. BertiniMaria do Carmo NicolettiLiang ZhaoPublished in: IEEE Congress on Evolutionary Computation (2013)
Keyphrases
- ensemble learning
- graph structure
- training data
- multiple classifiers
- classifier ensemble
- ensemble classifier
- training set
- ensemble pruning
- majority voting
- majority vote
- final classification
- individual classifiers
- weighted voting
- feature selection
- combining classifiers
- test set
- directed graph
- ensemble methods
- ensemble classification
- random walk
- randomized trees
- decision trees
- decision tree classifiers
- class imbalance
- weak classifiers
- diversity measures
- bias variance decomposition
- mining concept drifting data streams
- imbalanced data
- weak learners
- multiple classifier systems
- binary classification problems
- accurate classifiers
- class label noise
- class distribution
- learning algorithm
- support vector
- naive bayes
- linear classifiers
- machine learning algorithms
- neural network
- machine learning
- one class support vector machines
- feature set
- class labels
- bipartite graph
- graph theory
- classifier combination
- svm classifier
- base classifiers
- non stationary
- training examples
- random forest
- training samples
- trained classifiers
- concept drift
- weighted graph
- concept drifting data streams
- roc curve
- combining multiple