The following publications are possibly variants of this publication:
- Label distribution-based noise correction for multiclass crowdsourcingZiqi Chen, Liangxiao Jiang, Chaoqun Li. ijis, 37(9):5752-5767, 2022. [doi]
- Label confidence-based noise correction for crowdsourcingLijuan Ren, Liangxiao Jiang, Chaoqun Li 0001. eaai, 117(Part):105624, 2023. [doi]
- Noise correction of image labeling in crowdsourcingBryce Nicholson, Victor S. Sheng, Jing Zhang. icip 2015: 1458-1462 [doi]
- Label noise correction and application in crowdsourcingBryce Nicholson, Victor S. Sheng, Jing Zhang. eswa, 66:149-162, 2016. [doi]
- Worker similarity-based noise correction for crowdsourcingYufei Hu, Liangxiao Jiang, Wenjun Zhang. is, 121:102321, March 2024. [doi]
- Improving Label Quality in Crowdsourcing Using Noise CorrectionJing Zhang, Victor S. Sheng, Jian Wu, Xiaoqin Fu, Xindong Wu. CIKM 2015: 1931-1934 [doi]
- Resampling-based noise correction for crowdsourcingWenqiang Xu, Liangxiao Jiang, Chaoqun Li. jetai, 33(6):985-999, 2021. [doi]
- Improving label quality in crowdsourcing using deep co-teaching-based noise correctionKang Zhu, Siqing Xue, Liangxiao Jiang. mlc, 14(10):3641-3654, October 2023. [doi]
- Neighborhood Weighted Voting-Based Noise Correction for CrowdsourcingHuiru Li, Liangxiao Jiang, Siqing Xue. tkdd, 17(7), 2023. [doi]
- Instance difficulty-based noise correction for crowdsourcingYufei Hu, Liangxiao Jiang, Chaoqun Li. eswa, 212:118794, 2023. [doi]
- Three-way decision-based noise correction for crowdsourcingXue Wu, Liangxiao Jiang, Wenjun Zhang, Chaoqun Li 0001. ijar, 160:108973, September 2023. [doi]