The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Repeated labeling using multiple noisy labelers
Overview
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Citations
Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
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
Cost-sensitive learning by cost-proportionate example weighting
Zadrozny, B., Langford, J., & Abe, N. (2004) · Third IEEE International Conference on Data Mining · doi:10.1109/icdm.2003.1250950 · Cited by 680
Cited by
Dealing with Noise Problem in Machine Learning Data-sets: A Systematic Review
Gupta, S., & Gupta, A. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.11.146 · Cited by 325
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
Self-citationTheory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science
Radford, J., & Joseph, K. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00018 · Cited by 58
Crowdsourcing the Unknown: The Satellite Search for Genghis Khan
Lin, A. Y.-M., Huynh, A., Lanckriet, G., & Barrington, L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114046 · Cited by 35
A technical survey on statistical modelling and design methods for crowdsourcing quality control
Jin, Y., Carman, M., Zhu, Y., & Xiang, Y. (2020) · Artificial Intelligence · doi:10.1016/j.artint.2020.103351 · Cited by 28
Metadata
- Authors
- Ipeirotis, P. G., Provost, F., Sheng, V. S., & Wang, J.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2013-03-16
- Volume
- 28
- Issue
- 2
- Pages
- 402-441
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2147687736
- Cited by
- 181
- References
- 3
- FWCI
- 21.72
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Repeated labeling using multiple noisy labelers. https://app.cochat.ai/papers/10.1007/s10618-013-0306-1
- The paper
- Ipeirotis, P. G., Provost, F., Sheng, V. S., & Wang, J. (2014). Repeated labeling using multiple noisy labelers. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-013-0306-1
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
