Aggregation of Crowdsourced Labels Based on Worker History

@inproceedings{Georgescu2014AggregationOC,
  title={Aggregation of Crowdsourced Labels Based on Worker History},
  author={Mihai Georgescu and Xiaofei Zhu},
  booktitle={WIMS},
  year={2014}
}
Using crowdsourcing for gathering labels can be beneficial for supervised machine learning, if done in the right way. Crowdsourcing is more cost-effective and faster than employing experts for labeling the items needed as training examples. Unfortunately, the crowd produced labels are not always of a comparable quality. Therefore, different methods could be employed in order to assure label quality. One of them is redundancy, by gathering more than one label per item, from different assessors… CONTINUE READING

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