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所属单位：计算机科学与技术学院/人工智能学院/软件学院
发表刊物：IJCAI Int. Joint Conf. Artif. Intell.
摘要：In traditional active learning, there is only one labeler that always returns the ground truth of queried labels. However, in many applications, multiple labelers are available to offer diverse qualities of labeling with different costs. In this paper, we perform active selection on both instances and labelers, aiming to improve the classification model most with the lowest cost. While the cost of a labeler is proportional to its overall labeling quality, we also observe that different labelers usually have diverse expertise, and thus it is likely that labelers with a low overall quality can provide accurate labels on some specific instances. Based on this fact, we propose a novel active selection criterion to evaluate the cost-effectiveness of instance-labeler pairs, which ensures that the selected instance is helpful for improving the classification model, and meanwhile the selected labeler can provide an accurate label for the instance with a relative low cost. Experiments on both UCI and real crowdsourcing data sets demonstrate the superiority of our proposed approach on selecting cost-effective queries.
ISSN号：1045-0823
是否译文：否
发表时间：2017-01-01
合写作者：Chen, Jia-Lve,Mu, Xin,Zhou, Zhi-Hua
通讯作者：黄圣君,Chen, Jia-Lve,黄圣君