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Bagging

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Encyclopedia of Database Systems

Synonyms

Bootstrap aggregating

Definition

Bagging (Bootstrap Aggregating) uses “majority voting” to combine the output of different inductive models, constructed from bootstrap samples of the same training set. A bootstrap has the same size as the training data, and is uniformly sampled from the original training set with replacement. That is, after an example is selected from the training set, it is still kept in the training set for subsequent sampling and the same example could be selected multiple times into the same bootstrap sample. When the training set is sufficiently large, on average, a bootstrap sample has 63.2% unique examples from the original training set, and the rest are duplicates. In order to make full use of bagging, typically, one need to generate at least 50 bootstrap samples and construct 50 classifiers using these samples. During prediction, the class label receiving the most votes or most predictions from the base level 50 classifiers will be the final...

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Author information

Authors and Affiliations

  1. IBM T.J. Watson Research, Hawthorne, NY, USA

    Wei Fan

  2. Xavier University of Louisiana, New Orleans, LA, USA

    Kun Zhang

Authors
  1. Wei Fan
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  2. Kun Zhang
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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Fan, W., Zhang, K. (2009). Bagging. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_567

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