A generalised label noise model for classification in the presence of annotation errors

Supervised learning from annotated data is becoming more challenging due to inherent imperfection of training labels. Previous studies of learning in the presence of label noise have been focused on label noise which occurs randomly, while the study of label noise that is influenced by input features, which is intuitively more realistic, is still lacking. In this paper, we propose a new, generalised label noise model which is able to withstand the negative effect of random label noise and a wide range of non-random label noises. Empirical studies using a battery of synthetic data and four real-world datasets with inherent annotation errors demonstrate that the proposed generalised label noise model improves, in terms of classification accuracy, upon existing label noise modelling approaches.