In classification, it is often difficult or expensive to obtain completely accurate and reliable labels. Indeed, labels may be polluted by label noise, due to e.g. insufficient information, expert mistakes, and encoding errors. The problem is that errors in training labels that are not properly handled may deteriorate the accuracy of subsequent predictions, among other effects. Many works have been devoted to label noise and this paper provides a concise and comprehensive introduction to this research topic. In particular, it reviews the types of label noise, their consequences and a number of state of the art approaches to deal with label noise.
|Title of host publication||Proceedings of the 2014 European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2014)|
|Publication status||Published - 2014|
- Label noise ICTEAM: MLAI