2013/09/26 by Jakramate Bootkrajang, Bootkrajang, Jakramate, Ata Kabán +2
Computer Science · Engineering · Mathematics · #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1309.6818
Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)
arxiv created 2013/09/26 · openalex publication_date 2013/09/26 · arxiv updated 2013/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.