Boosting in the presence of label noise

Abstract

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.

Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)

References in corpus (2)

• Obtaining Calibrated Probabilities from Boosting
• A more robust boosting algorithm

Cited by in corpus (4)

• Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey
• Learning with Bounded Instance- and Label-dependent Label Noise
• Wavelet Decomposition of Gradient Boosting
• Margin Maximization as Lossless Maximal Compression