Uncertainty in Artificial Intelligence
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Boosting in the presence of label noise
Jakramate Bootkrajang, Ata Kaban
Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost's robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of robust classifiers, although converges faster than non label-noise aware AdaBoost, is still susceptible to label noise. However, pairing it with the new robust Boosting algorithm we propose here results in a more resilient algorithm under mislabelling.
Pages: 82-91
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PDF Link: /papers/13/p82-bootkrajang.pdf
AUTHOR = "Jakramate Bootkrajang and Ata Kaban",
TITLE = "Boosting in the presence of label noise",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "82--91"

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