In this paper we present a experimental evaluation of a boosting based learning system and show that can be run efficiently over a large dataset. The system uses as base learner decision stumps, single atribute decision trees with only two terminal nodes. To select the best decision stump at each iteration we use an adaptive sampling method. As a boosting algorithm, we use a modification of AdaBoost that is suitable to be combined with a base learner that does not use all the dataset. We provide experimental evidence that our method is as accurate as the equivalent algorithm that uses all the dataset but much faster.
CITATION STYLE
Domingo, C., & Watanabe, O. (2000). Scaling up a boosting-based learner via adaptive sampling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1805, pp. 317–328). Springer Verlag. https://doi.org/10.1007/3-540-45571-x_37
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