Boosting in the presence of label noise
This addresses robustness in boosting algorithms for machine learning practitioners, but it is incremental as it builds on existing AdaBoost methods.
The paper tackled boosting's sensitivity to label noise by testing robust base classifiers and a modified AdaBoost algorithm, finding that combining both yields a more resilient algorithm under mislabeling.
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.