An approach to error label discrimination based on joint clustering

Abstract

AbstractInaccurate multi-label learning aims at dealing with multi-label data with wrong labels. Error labels in data sets usually result in cognitive bias for objects. To discriminate and correct wrong labels is a significant issue in multi-label learning. In this paper, a joint discrimination model based on fuzzy C-means (FCM) and possible C-means (PCM) is proposed to find wrong labels in data sets. In this model, the connection between samples and their labels is analyzed based on the assumption of consistence between samples and their labels. Samples and labels are clustered by considering this connection in the joint FCM-PCM clustering model. An inconsistence measure between a sample and its label is established to recognize wrong labels. A series of simulated experiments are comparatively implemented on several real multi-label data sets and experimental results show superior performance of the proposed model in comparison with two state of the art methods of mislabeling correction.