Boosting in the presence of label noise [article]

Jakramate Bootkrajang, Ata Kaban
2013 arXiv   pre-print
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
more » ... we propose here results in a more resilient algorithm under mislabelling.
arXiv:1309.6818v1 fatcat:itq5a5uscrgzdggbu7yibohswq