Multiboosting: A technique for combining boosting and wagging
GI Webb - Machine learning, 2000 - Springer
Machine learning, 2000•Springer
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 …
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 …
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.
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