Bagging predictors View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1996-08

AUTHORS

Leo Breiman

ABSTRACT

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability of the prediction method. If perturbing the learning set can cause significant changes in the predictor constructed, then bagging can improve accuracy. More... »

PAGES

123-140

References to SciGraph publications

  • 1993. Learning classification trees in ARTIFICIAL INTELLIGENCE FRONTIERS IN STATISTICS
  • 1993. An Introduction to the Bootstrap in NONE
  • Journal

    TITLE

    Machine Learning

    ISSUE

    2

    VOLUME

    24

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00058655

    DOI

    http://dx.doi.org/10.1007/bf00058655

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1002929950


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