  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.
