Double weighted K-nearest voting for label aggregation in crowdsourcing learning

Author:

Li Jiaye,Yu Hao,Zhang Leyuan,Wen Guoqiu

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference43 articles.

1. Barbosa NM, Chen M (2019) Rehumanized crowdsourcing: a labeling framework addressing bias and ethics in machine learning

2. Bo H, Tsang IW, Chen L, Zhou JT, Yu Celina P (2019) Beyond majority voting: a coarse-to-fine label filtration for heavily noisy labels. IEEE Trans Neural Netw Learn Syst, 1–14

3. Chawla S, Hartline JD, Sivan Ba (2019) Optimal crowdsourcing contests. Games Econ Behav 113(C):80–96

4. Cohensius G, Porat OB, Meir R, Amir O (2018) Efficient crowdsourcing via proxy voting. arXiv: 1806.06257

5. Daniel F, Kucherbaev P, Cappiello C, Benatallah B, Allahbakhsh M (2018) Quality control in crowdsourcing: a survey of quality attributes, assessment techniques, and assurance actions. ACM Comput Surveys (CSUR) 51(1):7

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