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Ensemble Methods in Machine Learning

TitleEnsemble Methods in Machine Learning
Publication TypeConference Paper
Year of Publication2000
AuthorsDietterich, T. G.
Conference NameFirst International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science
Pagination1–15
Date Published06/2000
PublisherSpringer-Verlag
Conference LocationCagliari, Italy
Abstract

Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.