ML Anthology

Pruning Adaptive Boosting

Dragos D. Margineantu, Thomas G. Dietterich
ICML 1997 pp. 211-218
/icml/1997/margineantu1997icml-pruning/

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.

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Cite

Text

Margineantu and Dietterich. "Pruning Adaptive Boosting." International Conference on Machine Learning, 1997.

Markdown

[Margineantu and Dietterich. "Pruning Adaptive Boosting." International Conference on Machine Learning, 1997.](https://mlanthology.org/icml/1997/margineantu1997icml-pruning/)

BibTeX

@inproceedings{margineantu1997icml-pruning,
  title     = {{Pruning Adaptive Boosting}},
  author    = {Margineantu, Dragos D. and Dietterich, Thomas G.},
  booktitle = {International Conference on Machine Learning},
  year      = {1997},
  pages     = {211-218},
  url       = {https://mlanthology.org/icml/1997/margineantu1997icml-pruning/}
}