Instance difficulty-based noise correction for crowdsourcing
作者：
Highlights：
• Crowdsourcing provides an effective way to collect labels from crowd workers.
• Noise correction algorithms have been proposed to reduce the effect of noise.
• Existing algorithms seldom consider the effect of instance difficulty.
• This paper proposes an instance difficulty-based noise correction algorithm.
• The extensive experiments validate the effectiveness of the proposed algorithm.
摘要
•Crowdsourcing provides an effective way to collect labels from crowd workers.•Noise correction algorithms have been proposed to reduce the effect of noise.•Existing algorithms seldom consider the effect of instance difficulty.•This paper proposes an instance difficulty-based noise correction algorithm.•The extensive experiments validate the effectiveness of the proposed algorithm.
论文关键词：Crowdsourcing learning,Noise correction,Instance difficulty
论文评审过程：Received 25 July 2022, Revised 26 August 2022, Accepted 4 September 2022, Available online 7 September 2022, Version of Record 18 September 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118794
