An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naive Bayes.
Hung-Yi LoKai-Wei ChangShang-Tse ChenTsung-Hsien ChiangChun-Sung FerngCho-Jui HsiehYi-Kuang KoTsung-Ting KuoHung-Che LaiKen-Yi LinChia-Hsuan WangHsiang-Fu YuChih-Jen LinHsuan-Tien LinShou-De LinPublished in: KDD Cup (2009)
Keyphrases
- naive bayes
- linear model
- base classifiers
- decision trees
- combining classifiers
- ensemble learning
- ensemble classifier
- ensemble methods
- classification accuracy
- training data
- classifier ensemble
- text classification
- least squares
- logistic regression
- regression model
- classification algorithm
- feature selection
- base learners
- cost sensitive
- text categorization
- classifier combination
- linear models
- naive bayes classifier
- bayesian networks
- random forest
- classification error
- bayesian classifiers
- boosting algorithms
- individual classifiers
- bayesian classifier
- probabilistic classifiers
- decision tree learning algorithms
- naive bayesian classifier
- text classifiers
- boosted decision trees
- tree augmented naive bayes
- bayesian network classifiers
- multiple classifiers
- majority voting
- class distribution
- regression trees
- training set
- augmented naive bayes
- random forests
- class probabilities
- learning algorithm
- machine learning
- class labels