Boosting in the presence of label noise

Jakramate Bootkrajang, Ata Kaban
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:312-321, 2013.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-bootkrajang13a, title = {Boosting in the presence of label noise}, author = {Bootkrajang, Jakramate and Kaban, Ata}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {312--321}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/bootkrajang13a/bootkrajang13a.pdf}, url = {https://proceedings.mlr.press/r11/bootkrajang13a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Boosting in the presence of label noise %A Jakramate Bootkrajang %A Ata Kaban %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-bootkrajang13a %I PMLR %P 312--321 %U https://proceedings.mlr.press/r11/bootkrajang13a.html %V R11 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Bootkrajang, J. & Kaban, A.. (2013). Boosting in the presence of label noise. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:312-321 Available from https://proceedings.mlr.press/r11/bootkrajang13a.html. Reissued by PMLR on 04 October 2026.

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