The Effect of Structural Diversity of an Ensemble of Classifiers on Classification Accuracy
Lesedi Melton MasisiFulufhelo V. NelwamondoTshilidzi MarwalaPublished in: CoRR (2008)
Keyphrases
- classification accuracy
- classifier ensemble
- training data
- training set
- multiple classifier systems
- diversity measures
- naive bayes
- feature selection
- ensemble members
- support vector
- ensemble learning
- multiple classifiers
- feature set
- ensemble classifier
- ensemble methods
- ensemble pruning
- majority vote
- feature subset
- majority voting
- base classifiers
- improving classification accuracy
- combining classifiers
- individual classifiers
- concept drifting data streams
- classification rate
- class label noise
- accurate classification
- classifier combination
- high classification accuracy
- decision trees
- feature space
- classification algorithm
- random forest
- data sets
- training set size
- decision tree classifiers
- final classification
- imbalanced data
- support vector machine
- weak classifiers
- randomized trees
- weak learners
- mining concept drifting data streams
- bias variance decomposition
- one class support vector machines
- feature ranking
- correctly classified
- bayesian network classifiers
- generalization error
- classification models
- machine learning algorithms
- training samples
- learning algorithm