ELDP: Enhanced Label Distribution Propagation for Crowdsourcing

Abstract

In crowdsourcing scenarios, we can obtain multiple noisy labels for an instance from crowd workers and then aggregate these labels to infer the unknown true label of this instance. Due to the lack of expertise of workers, obtained labels usually contain a degree of noise. Existing studies usually focus on the crowdsourcing scenarios with low noise ratios but rarely focus on the crowdsourcing scenarios with high noise ratios. In this paper, we focus on the crowdsourcing scenarios with high noise ratios and propose a novel label aggregation algorithm called enhanced label distribution propagation (ELDP). First, ELDP harnesses an internal worker weighting method to estimate the weights of workers and then performs the first label distribution enhancement. Then, for instances not covered in the first enhancement, ELDP performs the second enhancement using a class membership estimation method based on the intra-cluster distance. Finally, ELDP propagates enhanced label distributions from accurately enhanced instances to inaccurately enhanced instances. Experimental results on both simulated and real-world crowdsourced datasets show that ELDP significantly outperforms all the other state-of-the-art label aggregation algorithms.