Lossless online Bayesian bagging

Published

Journal Article

© 2004 Herbert K. H. Lee and Merlise A. Clyde. Bagging frequently improves the predictive performance of a model. An online version has recently been introduced, which attempts to gain the benefits of an online algorithm while approximating regular bagging. However, regular online bagging is an approximation to its batch counterpart and so is not lossless with respect to the bagging operation. By operating under the Bayesian paradigm, we introduce an online Bayesian version of bagging which is exactly equivalent to the batch Bayesian version, and thus when combined with a lossless learning algorithm gives a completely lossless online bagging algorithm. We also note that the Bayesian formulation resolves a theoretical problem with bagging, produces less variability in its estimates, and can improve predictive performance for smaller data sets.

Full Text

Duke Authors

Cited Authors

  • Lee, HKH; Clyde, MA

Published Date

  • 2004

Published In

Volume / Issue

  • 5 /

Start / End Page

  • 143 - 151

Electronic International Standard Serial Number (EISSN)

  • 1533-7928

International Standard Serial Number (ISSN)

  • 1532-4435