I'm trying to model a binary classification problem. 5 continuous features, slightly imbalanced dataset (about 60 in one class, a little more than 200 in another).
So far I've tried kNN, LDA, and C5.0. C5.0 works very well (e.g. the AUC for ROC is very good).
However, I've read that boosting a la AdaBoost has problems in the presence of mislabeled data, and that C5.0's boosting is reminiscent of AdaBoost. My data is mislabeled insofar as perhaps 5% of them are probably wrong. (I'm vague here, because it's a real-world problem where the ground truth is fuzzy---good, but not perfect.)
Does anyone know how well C5.0 boosting performs in the presence of mislabeled data?