An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants (1999)

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by Eric Bauer , Ron Kohavi

Venue:	MACHINE LEARNING
Citations:	449 - 2 self

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Datum	Value	Source
TITLE	An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants	user correction - Legacy Corrections
AUTHOR NAME	Eric Bauer	user correction
AUTHOR AFFIL	Computer Science Department, Stanford University	user correction
AUTHOR ADDR	Stanford CA 94305	user correction
AUTHOR NAME	Ron Kohavi	user correction
AUTHOR AFFIL	Blue Martini Software	user correction
AUTHOR ADDR	2600 Campus Dr. Suite 175, San Mateo, CA 94403	user correction
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, reweighting, and combination techniques, affect classification error. We provide a bias and variance decomposition of the error to show how different methods and variants influence these two terms. This allowed us to determine that Bagging reduced variance of unstable methods, while boosting methods (AdaBoost and Arc-x4) reduced both the bias and variance of unstable methods but increased the variance for Naive-Bayes, which was very stable. We observed that Arc-x4 behaves differently than AdaBoost if reweighting is used instead of resampling, indicating a fundamental difference. Voting variants, some of which are introduced in this paper, include: pruning versus no pruning, use of probabilistic estimates, weight perturbations (Wagging), and backfitting of data. We found that Bagging improves when probabilistic estimates in conjunction with no-pruning are used, as well as when the data was backfit. We measure tree sizes and show an interesting positive correlation between the increase in the average tree size in AdaBoost trials and its success in reducing the error. We compare the mean-squared error of voting methods to non-voting methods and show that the voting methods lead to large and significant reductions in the mean-squared errors. Practical problems that arise in implementing boosting algorithms are explored, including numerical instabilities and underflows. We use scatterplots that graphically show how AdaBoost reweights instances, emphasizing not only "hard" areas but also outliers and noise.	user correction
YEAR	1999	user correction
VENUE	MACHINE LEARNING	user correction
VENUE TYPE	Journal	user correction
PAGES	105--139	user correction
VOLUME	36	user correction
TECH	DOI: 10.1023/A:1007515423169	user correction
CITATIONS	54 found	ParsCit 1.0