Towards instance-dependent label noise-tolerant classification: a probabilistic approach

Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. Existing label noise-tolerant learning machines were primarily designed to tackle class-conditional noise which occurs at random, independently from input instances. However, relatively less attention was given to a more general type of label noise which is influenced by input features. In this paper, we try to address the problem of learning a classifier in the presence of instance-dependent label noise by developing a novel label noise model which is expected to capture the variation of label noise rate within a class. This is accomplished by adopting a probability density function of a mixture of Gaussians to approximate the label flipping probabilities. Experimental results demonstrate the effectiveness of the proposed method over existing approaches.