Show simple item record

dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorHuynh, Huu Viet
dc.contributor.authorNguyen, Thanh Tam
dc.contributor.authorWeidlich, Matthias
dc.contributor.authorYin, Hongzhi
dc.contributor.authorZhou, Xiaofang
dc.date.accessioned2020-04-23T05:03:07Z
dc.date.available2020-04-23T05:03:07Z
dc.date.issued2018
dc.identifier.doi10.1109/icde.2018.00232
dc.identifier.urihttp://hdl.handle.net/10072/393373
dc.description.abstractCrowdsourcing has been widely established as a means to enable human computation at large-scale, in particular for tasks that require manual labelling of large sets of data items. Answers obtained from heterogeneous crowd workers are aggregated to obtain a robust result. In this paper, we consider partial-agreement tasks that are common in many applications such as image tagging and document annotation, where items are assigned sets of labels. Going beyond the state-of-the-art, we propose a novel Bayesian nonparametric model to aggregate the partial-agreement answers in a generic way. This model enables us to compute the consensus of partially-sound and partially-complete worker answers, while taking into account mutual relations in labels and different answer sets. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of precision, recall, and scalability.
dc.publisherIEEE
dc.relation.ispartofconferencename2018 IEEE 34th International Conference on Data Engineering (ICDE)
dc.relation.ispartofconferencetitle2018 IEEE 34th International Conference on Data Engineering (ICDE)
dc.relation.ispartofdatefrom2018-04-16
dc.relation.ispartofdateto2018-04-19
dc.subject.fieldofresearchData management and data science
dc.subject.fieldofresearchcode4605
dc.titleComputing Crowd Consensus with Partial Agreement
dc.typeConference output
dc.type.descriptionE3 - Conferences (Extract Paper)
dcterms.bibliographicCitationNguyen, QVH; Huynh, HV; Nguyen, TT; Weidlich, M; Yin, H; Zhou, X, Computing Crowd Consensus with Partial Agreement, 2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018
dc.date.updated2020-04-23T05:00:13Z
gro.hasfulltextNo Full Text
gro.griffith.authorNguyen, Henry
gro.griffith.authorNguyen, Thanh Tam


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record