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Tree size determination for classification ensemble
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 Title & Authors
Tree size determination for classification ensemble
Choi, Sung Hoon; Kim, Hyunjoong;
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Classification is a predictive modeling for a categorical target variable. Various classification ensemble methods, which predict with better accuracy by combining multiple classifiers, became a powerful machine learning and data mining paradigm. Well-known methodologies of classification ensemble are boosting, bagging and random forest. In this article, we assume that decision trees are used as classifiers in the ensemble. Further, we hypothesized that tree size affects classification accuracy. To study how the tree size in uences accuracy, we performed experiments using twenty-eight data sets. Then we compare the performances of ensemble algorithms; bagging, double-bagging, boosting and random forest, with different tree sizes in the experiment.
Bagging;boosting;classification;decision tree;double-bagging;ensemble;random forest;
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