Active Learning for Crowdsourcing Using Knowledge Transfer

  title={Active Learning for Crowdsourcing Using Knowledge Transfer},
  author={Meng Fang and Jie Yin and Dacheng Tao},
This paper studies the active learning problem in crowdsourcing settings, where multiple imperfect annotators with varying levels of expertise are available for labeling the data in a given task. Annotations collected from these labelers may be noisy and unreliable, and the quality of labeled data needs to be maintained for data mining tasks. Previous solutions have attempted to estimate individual users’ reliability based on existing knowledge in each task, but for this to be effective each… CONTINUE READING
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