A Multi-agent Ensemble of Classifiers.
Jaime CalderónOmar López-OrtegaFélix Agustín Castro EspinozaPublished in: MICAI (1) (2015)
Keyphrases
- multi agent
- ensemble learning
- ensemble classifier
- ensemble pruning
- classifier ensemble
- multiple classifiers
- majority voting
- training data
- combining classifiers
- training set
- ensemble methods
- randomized trees
- majority vote
- final classification
- individual classifiers
- feature selection
- decision trees
- class label noise
- multiple classifier systems
- decision tree classifiers
- cooperative
- accurate classifiers
- ensemble members
- weak classifiers
- weighted voting
- diversity measures
- concept drifting data streams
- bias variance decomposition
- classifier combination
- base classifiers
- random forests
- classifier fusion
- naive bayes
- support vector
- feature ranking
- weak learners
- pruning method
- multi agent systems
- pruning algorithm
- reinforcement learning
- one class support vector machines
- publicly available data sets
- svm classifier
- machine learning methods
- classification models
- random forest
- test set
- mining concept drifting data streams
- machine learning
- generalization ability
- ensemble classification
- text categorization
- feature set
- classification accuracy
- concept drift
- linear classifiers
- fusion method
- training samples
- rule induction algorithm
- classification algorithm
- feature space
- individual features