Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias?Variance Analysis.
Giorgio ValentiniPublished in: Multiple Classifier Systems (2004)
Keyphrases
- ensemble methods
- bias variance analysis
- random forests
- base classifiers
- support vector
- majority voting
- generalization ability
- maximum margin
- imbalanced data
- decision trees
- machine learning
- ensemble learning
- prediction accuracy
- regression trees
- random forest
- multi class
- benchmark datasets
- support vector machine
- random subspaces
- training data
- machine learning methods
- feature selection
- training set
- classifier ensemble
- regression problems
- support vector machine svm
- bias variance
- knn
- logistic regression
- linear svm
- search space
- feature space
- sparse kernel
- data sets
- classification using support vector machines
- learning machines
- support vectors
- cost sensitive
- cross validation
- naive bayes
- k nearest neighbor
- classification accuracy
- probabilistic model
- evolutionary algorithm
- learning algorithm