Bagging, Boosting, and C4.5

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Bagging, Boosting, and C4.5 (1996)

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by J. R. Quinlan

Venue:	In Proceedings of the Thirteenth National Conference on Artificial Intelligence
Citations:	326 - 1 self

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@INPROCEEDINGS{Quinlan96bagging,boosting,,
    author = {J. R. Quinlan},
    title = {Bagging, Boosting, and C4.5},
    booktitle = {In Proceedings of the Thirteenth National Conference on Artificial Intelligence},
    year = {1996},
    pages = {725--730},
    publisher = {AAAI Press}
}

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Abstract

Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classifier learning systems. Both form a set of classifiers that are combined by voting, bagging by generating replicated bootstrap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that learns decision trees and testing on a representative collection of datasets. While both approaches substantially improve predictive accuracy, boosting shows the greater benefit. On the other hand, boosting also produces severe degradation on some datasets. A small change to the way that boosting combines the votes of learned classifiers reduces this downside and also leads to slightly better results on most of the datasets considered. Introduction Designers of empirical machine learning systems are concerned with such issues as the computational cost of the learning method and the accuracy and ...

Keyphrases

small change predictive accuracy decision tree bootstrap sample predictive power severe degradation representative collection recent method empirical machine learned classifier training instance computational cost introduction designer learning method

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