MultiBoosting: A Technique for Combining Boosting and Wagging

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. Usin...

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Bibliographic Details
Published inMachine learning Vol. 40; no. 2; pp. 159 - 196
Main Author Webb, Geoffrey I
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Nature B.V 01.08.2000
Subjects
Algorithms
Studies
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Summary: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.[PUBLICATION ABSTRACT]
Bibliography:ObjectType-Article-2
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ISSN:0885-6125
1573-0565
DOI:10.1023/A:1007659514849