Using a Neural Network to Approximate an Ensemble of Classifiers.
Xinchuan ZengTony R. MartinezPublished in: Neural Process. Lett. (2000)
Keyphrases
- neural network
- ensemble learning
- ensemble classifier
- multiple classifiers
- training data
- classifier ensemble
- ensemble pruning
- majority voting
- final classification
- multiple classifier systems
- multi layer perceptron
- training set
- individual classifiers
- weighted voting
- ensemble methods
- majority vote
- combining classifiers
- akaike information criterion
- randomized trees
- machine learning algorithms
- accurate classifiers
- feature selection
- diversity measures
- decision trees
- support vector
- nearest neighbour
- linear classifiers
- back propagation
- artificial neural networks
- pattern recognition
- weak classifiers
- decision tree classifiers
- neural network ensemble
- weak learners
- trained classifiers
- concept drifting data streams
- class label noise
- feature ranking
- naive bayes
- test set
- base classifiers
- classification algorithm
- neural network model
- classifier combination
- publicly available data sets
- training samples
- ensemble classification
- ensemble members
- generalization ability
- bp neural network
- classification models
- one class support vector machines
- benchmark datasets
- bias variance decomposition
- training process
- random forests
- mining concept drifting data streams
- learning algorithm