LAUSR

Crowdsourcing services provide an efficient and relatively inexpensive approach to obtain substantial amounts of labeled data by employing crowd workers. It is obvious that the labeling qualities of crowd workers directly affect the quality of the labeled data. However, existing label aggregation strategies seldom consider the differences in the quality of workers labeling different instances. In this paper, we argue that a single worker may even have different labeling qualities on different instances. Based on this premise, we propose four new strategies by assigning different weights to workers when labeling different instances. In our proposed strategies, we first use the similarity among worker labels to estimate the specific quality of the worker on different instances, and then we build a classifier to estimate the overall quality of the worker across all instances. Finally, we combine these two qualities to define the weight of the worker labeling a particular instance. Extensive experimental results show that our proposed strategies significantly outperform other existing state-of-the-art label aggregation strategies.