Learning from Crowds and Experts

Crowdsourcing services are often used to collect a large amount of labeled data for machine learning. Although they provide us an easy way to get labels at very low cost in a short period, they have serious limitations. One of them is the variable quality of the crowd-generated data. There have been many attempts to increase the reliability of crowd-generated data and the quality of classifiers obtained from such data. However, in these problem settings, relatively few researchers have tried using expert-generated data to achieve further improvements. In this paper, we extend three models that deal with the problem of learning from crowds to utilize ground truths: a latent class model, a personal classifier model, and a data-dependent error model. We evaluate the proposed methods against two baseline methods on a real data set to demonstrate the effectiveness of combining crowd-generated data and expert-generated data.