The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Class Noise vs. Attribute Noise: A Quantitative Study
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
(1992) · Cited by 23,431
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Statistical Analysis With Missing Data
Lahiff, M., Little, R. J. A., & Rubin, D. B. (1989) · Journal of the American Statistical Association · doi:10.2307/2289883 · Cited by 17,265
UCI Repository of machine learning databases
Blake, C. L. (1998) · Medical Entomology and Zoology · Cited by 10,582
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences.
Tufte, E. R., Cohen, J., & Cohen, P. (1979) · Journal of the American Statistical Association · doi:10.2307/2286442 · Cited by 5,180
Beyond Accuracy: What Data Quality Means to Data Consumers
Wang, R. Y., & Strong, D. M. (1996) · Journal of Management Information Systems · doi:10.1080/07421222.1996.11518099 · Cited by 4,672
Statistical Analysis With Missing Data
Ghosh, S. (1988) · Technometrics · doi:10.1080/00401706.1988.10488446 · Cited by 4,576
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences.
Fong, G. T., Cohen, J., & Cohen, P. (1985) · Journal of the American Statistical Association · doi:10.2307/2287930 · Cited by 4,475
Beckman, R. J. (1987) · Technometrics · doi:10.1080/00401706.1987.10488195 · Cited by 2,835
Clark, P., & Niblett, T. (1989) · Machine Learning · doi:10.1007/bf00116835 · Cited by 2,267
Cited by
Classification in the Presence of Label Noise: A Survey
Frenay, B., & Verleysen, M. (2014) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2013.2292894 · Cited by 1,777
A unifying view on dataset shift in classification
Moreno-Torres, J. G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N. V., & Herrera, F. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2011.06.019 · Cited by 1,029
Tutorial on practical tips of the most influential data preprocessing algorithms in data mining
García, S., Luengo, J., & Herrera, F. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.006 · Cited by 310
Data preprocessing in predictive data mining
Alexandropoulos, S.-A. N., Kotsiantis, S. B., & Vrahatis, M. N. (2019) · The Knowledge Engineering Review · doi:10.1017/s026988891800036x · Cited by 209
The Effects and Interactions of Data Quality and Problem Complexity on Classification
Blake, R., & Mangiameli, P. (2011) · Journal of Data and Information Quality · doi:10.1145/1891879.1891881 · Cited by 132
Metadata
- Authors
- Zhu, X., & Wu, X.
- Venue
- Artificial Intelligence Review
- Published
- 2004-11
- Volume
- 22
- Issue
- 3
- Pages
- 177-210
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2034841618
- Cited by
- 893
- References
- 42
- FWCI
- 9.83
- 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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Class Noise vs. Attribute Noise: A Quantitative Study. https://app.cochat.ai/papers/10.1007/s10462-004-0751-8
- The paper
- Zhu, X., & Wu, X. (2004). Class Noise vs. Attribute Noise: A Quantitative Study. Artificial Intelligence Review. https://doi.org/10.1007/s10462-004-0751-8
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.
