Incremental Relabeling for Active Learning with Noisy Crowdsourced Annotations

  title={Incremental Relabeling for Active Learning with Noisy Crowdsourced Annotations},
  author={Liyue Zhao and Gita Reese Sukthankar and Rahul Sukthankar},
  journal={2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing},
Crowd sourcing has become an popular approach for annotating the large quantities of data required to train machine learning algorithms. However, obtaining labels in this manner poses two important challenges. First, naively labeling all of the data can be prohibitively expensive. Second, a significant fraction of the annotations can be incorrect due to carelessness or limited domain expertise of crowd sourced workers. Active learning provides a natural formulation to address the former issue… CONTINUE READING
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