A multi-view-based noise correction algorithm for crowdsourcing learning

Abstract

Crowdsourcing services provide a way to obtain large amounts of labeled data, which is inexpensive and effective. In crowdsourcing scenarios, the integrated labels of instances can be deduced by implementing ground truth inference algorithms. However, those labels often contain substantial noise and, to mitigate the effects of noise, label noise handling techniques are needed. This paper proposes a novel multi-view-based noise correction algorithm (MVNC). MVNC introduces the idea of multi-view learning to make better use of the information from crowdsourced data. It adds a new view composed of multiple noise labels and then trains classifiers on two views respectively to correct noise instances. In this process, different information between the views is fully utilized to generate the disagreement between two classifiers, so that the classifiers can complement each other and make more reliable predictions for noise instances. Experimental results on 38 benchmark data sets and 6 real-world data sets show that the new view significantly enhances the effect of noise correction.