Uncertainty in Artificial Intelligence
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Boosting in the presence of label noise
Jakramate Bootkrajang, Ata Kaban
Abstract:
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.
Keywords:
Pages: 82-91
PS Link:
PDF Link: /papers/13/p82-bootkrajang.pdf
BibTex:
@INPROCEEDINGS{Bootkrajang13,
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)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "82--91"
}


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