Boosting in the presence of label noise
Machine Learning
2013-09-27 v1 Machine Learning
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
@article{arxiv.1309.6818,
title = {Boosting in the presence of label noise},
author = {Jakramate Bootkrajang and Ata Kaban},
journal= {arXiv preprint arXiv:1309.6818},
year = {2013}
}
Comments
Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)