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Preview of Hierarchical Crowdsourcing
🔗 Source: dl.soc.i.kyoto-u.ac.jp
📊 Size: 731 KB
👤 Author: PC-A372
⬇️ Downloads: 84

Summary

Researchers propose a hierarchical scheme for multiclass classification in crowdsourcing, where different workers are assigned to different subtasks based on their strengths. They generate candidate hierarchical schemes, simulate worker allocation, and choose the scheme with the best accuracy. Experiments on Amazon Mechanical Turk show that hierarchical schemes improve accuracy when an appropriate hierarchy is chosen. The worker allocation algorithm uses a greedy approach, prioritizing workers with high variance in accuracy across tasks. Results from two experiments demonstrate the effectiveness of the hierarchical approach, outperforming flat classification with majority voting and EM-based weighted voting.

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Description

Researchers propose a hierarchical scheme for multiclass classification in crowdsourcing, where different workers are assigned to different subtasks based on...

Technical Information

  • File Format: PDF
  • File Size: 731 KB
  • Pages: 1
  • Language: EN
  • Author: PC-A372
  • Total Downloads: 84
  • Last Updated: 4 hours ago

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