Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model.

While crowdsourcing has become an important means to label data, crowdworkers are not always experts---sometimes they can even be adversarial. Therefore, there is great interest in estimating the ground truth from unreliable labels produced by crowdworkers. The Dawid and Skene (DS) model is one of the most well-known models in the study of crowdsourcing. Despite its practical popularity, theoretical error analysis for the DS model has been conducted only under restrictive assumptions on, e.g., class priors, confusion matrices, and the number of labels each worker provides. In this paper, we derive a minimax error rate under more practical setting for a broader class of crowdsourcing models that includes the DS model as a special case. We further propose the worker clustering model, which is more practical than the DS model under real crowdsourcing settings. Note that the wide applicability of our theoretical analysis allows us to immediately investigate the behavior of this proposed model. Experimental results showed that there is a strong similarity between the lower bound of the minimax error rate derived by our theoretical analysis and the empirical error of the estimated value.