Improving Label Quality in Crowdsourcing Using Noise Correction
Jing Zhang, Victor S. Sheng, Jian Wu, Xiaoqin Fu, Xindong Wu · 2015
This paper proposes a novel framework that introduces noise correction techniques to further improve label quality after ground truth inference in crowdsourcing. In the framework, an adaptive voting noise correction algorithm (AVNC) is proposed to identify and correct the most likely noises with the help of estimated qualities of labelers provided by the ground truth inference. The experimental results on two real-world datasets show that (1) the framework can improve label quality regardless of inference algorithms, especially under the circumstance that each example has a few noisy labels; and (2) since the algorithm AVNC considers both the number of and the probability of potential noises, it outperforms a baseline noise correction algorithm.