The sensitivity of Adaboost to random label noise is a well-studied problem. LogitBoost, BrownBoost and RobustBoost are boosting algorithms claimed to be
less sensitive to noise than AdaBoost...
We present the results of experiments
evaluating these algorithms on both synthetic and real datasets. We compare the
performance on each of datasets when the labels are corrupted by different
levels of independent label noise. In presence of random label noise, we found
that BrownBoost and RobustBoost perform significantly better than AdaBoost and
LogitBoost, while the difference between each pair of algorithms is
insignificant. We provide an explanation for the difference based on the margin
distributions of the algorithms.