The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Supervised learning from multiple experts: whom to trust when everyone lies a bit
Abstract
We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.
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Cites
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm
Dawid, A. P., & Skene, A. M. (1979) · Applied Statistics · doi:10.2307/2346806 · Cited by 1,623
A Simple Approach to Ordinal Classification
Frank, E., & Hall, M. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-44795-4_13 · Cited by 606
Cited by
Li, Y., Gao, J., Meng, C., Li, Q., Su, L., Zhao, B., Fan, W., & Han, J. (2016) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2897350.2897352 · Cited by 336
Majkowska, A., Mittal, S., Steiner, D. F., Reicher, J. J., McKinney, S. M., Duggan, G. E., Eswaran, K., Cameron Chen, P.-H., Liu, Y., Kalidindi, S. R., et al. (2020) · Radiology · doi:10.1148/radiol.2019191293 · Cited by 277
Crowdsourced Data Management: A Survey
Li, G., Wang, J., Zheng, Y., & Franklin, M. J. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2535242 · Cited by 283
Learning from crowdsourced labeled data: a survey
Zhang, J., Wu, X., & Sheng, V. S. (2016) · Artificial Intelligence Review · doi:10.1007/s10462-016-9491-9 · Cited by 203
Understanding and Mitigating Worker Biases in the Crowdsourced Collection of Subjective Judgments
Hube, C., Fetahu, B., & Gadiraju, U. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300637 · Cited by 129
Metadata
- Authors
- Raykar, V. C., Yu, S., Zhao, L. H., Jerebko, A., Florin, C., Valadez, G. H., Bogoni, L., & Moy, L.
- Venue
- Proceedings of the 26th Annual International Conference on Machine Learning
- Publisher
- ACM
- Published
- 2009-06-14
- Pages
- 889-896
- OpenAlex
- W2010135967
- Cited by
- 333
- References
- 3
- FWCI
- 16.92
- Type
- conference paper
- Language
- en
- Open access
- closed
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- The paper
- Raykar, V. C., Yu, S., Zhao, L. H., Jerebko, A., Florin, C., Valadez, G. H., Bogoni, L., & Moy, L. (2009). Supervised learning from multiple experts: whom to trust when everyone lies a bit. Proceedings of the 26th Annual International Conference on Machine Learning. https://doi.org/10.1145/1553374.1553488
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