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2015 IEEE International Conference on Data Mining (ICDM) (2015)
Atlantic City, NJ, USA
Nov. 14, 2015 to Nov. 17, 2015
ISSN: 1550-4786
ISBN: 978-1-4673-9503-8
pp: 937-942
Repeated labeling is a widely adopted quality control method in crowdsourcing. This method is based on selecting one reliable label from multiple labels collected by workers because a single label from only one worker has a wide variance of accuracy. Hierarchical classification, where each class has a hierarchical relationship, is a typical task in crowdsourcing. However, direct applications of existing methods designed for multi-class classification have the disadvantage of discriminating among a large number of classes. In this paper, we propose a label aggregation method for hierarchical classification tasks. Our method takes the hierarchical structure into account to handle a large number of classes and estimate worker abilities more precisely. Our method is inspired by the steps model based on item response theory, which models responses of examinees to sequentially dependent questions. We considered hierarchical classification to be a question consisting of a sequence of subquestions and built a worker response model for hierarchical classification. We conducted experiments using real crowdsourced hierarchical classification tasks and demonstrated the benefit of incorporating a hierarchical structure to improve the label aggregation accuracy.
Labeling, Crowdsourcing, Probabilistic logic, Pharmaceuticals, Reliability, Electronic mail, Quality control

N. Otani, Y. Baba and H. Kashima, "Quality Control for Crowdsourced Hierarchical Classification," 2015 IEEE International Conference on Data Mining (ICDM), Atlantic City, NJ, USA, 2015, pp. 937-942.
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