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A Label Completion Approach to Crowd Approximation

  • Toshihiro Watanabe
  • Hisashi Kashima
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8835)

Abstract

Majority vote is one of the most common methods for crowdsourced label aggregation to get higher-quality labels. In this paper, we extend the work of Donmez et al. that estimates majority labels with a small subset of crowdsourcing workers in order to reduce financial and time costs. Our proposed method estimates the majority labels more accurately by completing missing labels to approximate the whole crowds even if some workers do not answer labels. Experimental results show that the proposed method approximates crowds more accurately than the method without label completion.

Keywords

Recommendation System Majority Vote Computer Support Cooperative Work Neural Information Processing System Probabilistic Matrix Factorization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Toshihiro Watanabe
    • 1
  • Hisashi Kashima
    • 2
  1. 1.The University of TokyoTokyoJapan
  2. 2.Kyoto UniversityKyotoJapan

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