NDLI: Bagging Equalizes Influence

Content Provider	Springer Nature Link
Author	Grandvalet, Yves
Copyright Year	2004
Abstract	Bagging constructs an estimator by averaging predictors trained on bootstrap samples. Bagged estimates almost consistently improve on the original predictor. It is thus important to understand the reasons for this success, and also for the occasional failures. It is widely believed that bagging is effective thanks to the variance reduction stemming from averaging predictors. However, seven years from its introduction, bagging is still not fully understood. This paper provides experimental evidence supporting the hypothesis that bagging stabilizes prediction by equalizing the influence of training examples. This effect is detailed in two different frameworks: estimation on the real line and regression. Bagging’s improvements/deteriorations are explained by the goodness/badness of highly influential examples, in situations where the usual variance reduction argument is at best questionable. Finally, reasons for the equalization effect are advanced. They support that other resampling strategies such as half-sampling should provide qualitatively identical effects while being computationally less demanding than bootstrap sampling.
Starting Page	251
Ending Page	270
Page Count	20
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	55
Issue Number	3
e-ISSN	15730565
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2004-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computing Methodologies Artificial Intelligence (incl. Robotics) Simulation and Modeling Language Translation and Linguistics Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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