Machine Learning

, Volume 40, Issue 2, pp 159–196

MultiBoosting: A Technique for Combining Boosting and Wagging

  • Geoffrey I. Webb
Article

DOI: 10.1023/A:1007659514849

Cite this article as:
Webb, G.I. Machine Learning (2000) 40: 159. doi:10.1023/A:1007659514849

Abstract

MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees. MultiBoosting can be viewed as combining AdaBoost with wagging. It is able to harness both AdaBoost's high bias and variance reduction with wagging's superior variance reduction. Using C4.5 as the base learning algorithm, MultiBoosting is demonstrated to produce decision committees with lower error than either AdaBoost or wagging significantly more often than the reverse over a large representative cross-section of UCI data sets. It offers the further advantage over AdaBoost of suiting parallel execution.

boostingbaggingwaggingaggregationdecision committeedecision tree
Download to read the full article text

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Geoffrey I. Webb
    • 1
  1. 1.School of Computing and MathematicsDeakin UniversityGeelongAustralia