Name
Abstract | ||
|---|---|---|
With the fast growing of artificial intelligence (AI), more and more applications require querying uncertain data, especially from social media and crowd sourcing platform. In situations where it is impossible to increase data quality by controlling the sources, we may resort to algorithms to make the best use of the collected data. Since crowdsourcing provides a useful way to distributing tasks to mass people, and collects labels from as many workers as possible, many researchers have been study crowd-sourcing inference algorithms. In our work, we propose a novel crowd-sourcing inference algorithm to infer ground truth and obtain worker reliability and task difficulty at the same time. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IWCMC.2019.8766596 | 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC) |
Keywords | Field | DocType |
Truth Inference,Crowdsourcing,Message Passing | Data quality,Social media,Inference,Computer science,Crowdsourcing,Uncertain data,Ground truth,Artificial intelligence,Machine learning,Message passing,Distributed computing | Conference |
ISSN | ISBN | Citations |
2376-6492 | 978-1-5386-7748-3 | 0 |
PageRank | References | Authors |
0.34 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia Liu | 1 | 9 | 0.75 |
| William C. Tang | 2 | 0 | 0.68 |
| Yuanfang Chen | 3 | 169 | 31.96 |
| Mingchu Li | 4 | 469 | 78.10 |
| Mohsen Guizani | 5 | 6456 | 557.44 |