Pruning Adaptive Boosting

Abstract

The boosting algorithm AdaBoost, developed by Freund and Schapire, has exhibited outstanding performance on several benchmark problems when using C4.5 as the "weak" algorithm to be "boosted." Like other ensemble learning approaches, AdaBoost constructs a composite hypothesis by voting many individual hypotheses. In practice, the large amount of memory required to store these hypotheses can make ensemble methods hard to deploy in applications. This paper shows that by selecting a subset of the hypotheses, it is possible to obtain nearly the same levels of performance as the entire set. The results also provide some insight into the behavior of AdaBoost.