Label Aggregation for Crowdsourcing with Bi-Layer Clustering

Jing Zhang, Victor S. Sheng, Tao Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

This paper proposes a novel general label aggregation method for both binary and multi-class labeling in crowdsourcing, namely Bi-Layer Clustering (BLC), which clusters two layers of features-The conceptual-level and the physical-level features-To infer true labels of instances. BLC first clusters the instances using the conceptual-level features extracted from their multiple noisy labels and then performs clustering again using the physical-level features. It can facilitate tracking the uncertainty changes of the instances, so that the integrated labels that are likely to be falsely inferred on the conceptual layer can be easily corrected using the estimated labels on the physical layer. Experimental results on two real-world crowdsourcing data sets show that BLC outperforms seven state-of-The-Art methods.

Original languageEnglish
Title of host publicationSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages921-924
Number of pages4
ISBN (Electronic)9781450350228
DOIs
StatePublished - Aug 7 2017
Event40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan
Duration: Aug 7 2017Aug 11 2017

Publication series

NameSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval

Conference

Conference40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
CountryJapan
CityTokyo, Shinjuku
Period08/7/1708/11/17

Keywords

  • Clustering
  • Crowdsourcing
  • Inference
  • Label aggregation

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