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Abstract: Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several variants in conjunction with a decision tree inducer (three variants) and a Naive-Bayes inducer. The purpose of the study is to improve our understanding of why and when these algorithms, which use perturbation,... (Update)
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BibTeX entry: (Update)
E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, Boosting, and variants. To appear in Machine Learning (available at: http://reality.sgi.com/ronnyk/vote.ps.gz), 1998. http://citeseer.ist.psu.edu/bauer99empirical.html More
@article{ bauer99empirical,
author = "Eric Bauer and Ron Kohavi",
title = "An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants",
journal = "Machine Learning",
volume = "36",
number = "1-2",
pages = "105-139",
year = "1999",
url = "citeseer.ist.psu.edu/bauer99empirical.html" }
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