Error Rate Analysis of Labeling by Crowdsourcing
Hong-Wei Li · 2013
Crowdsourcing label generation has been a crucial component for many real-world ma-chine learning applications. In this paper, we provide finite-sample exponential bounds on the error rate (in probability and in ex-pectation) of hyperplane binary labeling rules for the Dawid-Skene (and Symmetric Dawid-Skene) crowdsourcing model. The bounds can be applied to analyze many commonly used prediction methods, including the ma-jority voting, weighted majority voting and maximum a posteriori (MAP) rules. These bound results can be used to control the error rate and design better algorithms. In particu-lar, under the Symmetric Dawid-Skene model we use simulation to demonstrate that the data-driven EM-MAP rule is a good approx-imation to the oracle MAP rule which ap-proximately optimizes our upper bound on the mean error rate for any hyperplane bi-nary labeling rule. Meanwhile, the average error rate of the EM-MAP rule is bounded well by the upper bound on the mean error rate of the oracle MAP rule in the simulation. 1.