Boosting in the presence of label noise

Understand

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.