Bagging Can Stabilize without Reducing Variance View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2001-08-17

AUTHORS

Yves Grandvalet

ABSTRACT

Bagging is a procedure averaging estimators trained on bootstrap samples. Numerous experiments have shown that bagged estimates almost consistently yield better results than the original predictor. It is thus important to understand the reasons for this success, and also for the occasional failures. Several arguments have been given to explain the effectiveness of bagging, among which the original “bagging reduces variance by averaging” is widely accepted. This paper provides experimental evidence supporting another explanation, based on the stabilization provided by spreading the influence of examples. With this viewpoint, bagging is interpreted as a case-weight perturbation technique, and its behavior can be explained when other arguments fail. More... »

PAGES

49-56

Book

TITLE

Artificial Neural Networks — ICANN 2001

ISBN

978-3-540-42486-4
978-3-540-44668-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-44668-0_8

DOI

http://dx.doi.org/10.1007/3-540-44668-0_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1009515612


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