文章基本信息

标题：Learning from Crowds and Experts
本地全文：下载
作者：Hiroshi Kajino ; Yuta Tsuboi ; Issei Sato 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2013
卷号：28
期号：3
页码：243-248
DOI：10.1527/tjsai.28.243
出版社：The Japanese Society for Artificial Intelligence
摘要：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 apply three models that deal with the problem of learning from crowds to this problem: a latent class model, a personal classifier model, and a data-dependent error model. We evaluate these methods against two baseline methods on a real data set to demonstrate the effectiveness of combining crowd-generated data and expert-generated data.
关键词：crowdsourcing ; machine learning