A generalised label noise model for classification

Abstract

Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. In this paper, we propose a new, generalised label noise model which is able to withstand the negative effect of both random noise and a wide range of non-random label noises. Empirical studies using three real-world datasets with inherent annotation errors demonstrate that the proposed generalised label noise model improves, in terms of classification accuracy, over existing label noise modelling approaches.