The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
On Studying the Effect of Data Quality on Classification Performances
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“Another dominant topic is completeness to which the ML community almost exclusively refers to as missing data119,125–128,133–135,182.”
From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [119]
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“When it comes to the concrete behaviour change of the DL algorithm, most of the DL papers in the literature corpus investigate the robustness of a model, i.e. the stable behaviour of a model when facing erroneous or a limited amount of inputs. Only few records investigate generalisability119,144,145 or distribution shift139,192, a model’s capability of coping with new, unseen data.”
From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [119]
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Cites
Data Cleaning: Overview and Emerging Challenges
Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (2016) · Proceedings of the 2016 International Conference on Management of Data · doi:10.1145/2882903.2912574 · Cited by 488
HoloClean: holistic data repairs with probabilistic inference
Rekatsinas, T., Chu, X., Ilyas, I. F., & Ré, C. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137628.3137631 · Cited by 480
2022 IEEE 38th International Conference on Data Engineering (ICDE)
(2022) · doi:10.1109/icde53745.2022 · Cited by 284
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing
Chu, X., Morcos, J., Ilyas, I. F., Ouzzani, M., Papotti, P., Tang, N., & Ye, Y. (2015) · Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data · doi:10.1145/2723372.2749431 · Cited by 279
Detecting data errors: where are we and what needs to be done?
Abedjan, Z., Chu, X., Deng, D., Fernandez, R. C., Ilyas, I. F., Ouzzani, M., Papotti, P., Stonebraker, M., & Tang, N. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/2994509.2994518 · Cited by 251
REPAIR: Removing Representation Bias by Dataset Resampling
Li, Y., & Vasconcelos, N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00980 · Cited by 223
CleanML: A Study for Evaluating the Impact of Data Cleaning on ML Classification Tasks
Li, P., Rao, S. X., Blase, J. L., Zhang, Y., Chu, X., & Zhang, C. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00009 · Cited by 201
ZeroER: Entity Resolution using Zero Labeled Examples
Wu, R., Chaba, S., Sawlani, S., Chu, X., & Thirumuruganathan, S. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3389743 · Cited by 103
Pattern functional dependencies for data cleaning
Qahtan, A. A., Tang, N., Ouzzani, M., Cao, Y., & Stonebraker, M. R. (2020) · Proceedings of the VLDB Endowment · doi:10.14778/3377369.3377377 · Cited by 43
Chu, X., & Ilyas, I. F. (2016) · Proceedings of the VLDB Endowment · doi:10.14778/3007263.3007320 · Cited by 39
Research works printed but not linked: 4
- Chu, Xu, I.F.I., Papotti, P.: Holistic data cleaning: putting violations into context. In: IEEE 29th International Conference on Data Engineering (ICDE) (2013)
- Jouseau, R., Salva, S., Samir, C.: Technical report for: on studying the effect of data quality on classification performance. Technical report, University of Clermont Auvergne, September 2022. https://hal.archives-ouvertes.fr/hal-03782760
- Krishnan, S., Franklin, M.J., Goldberg, K., Wu, E.: Boostclean: automated error detection and repair for machine learning. arXiv preprint arXiv:1711.01299 (2017) · arXiv
- Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Cited by
The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review
Schwabe, D., Becker, K., Seyferth, M., Klaß, A., & Schaeffter, T. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01196-4 · Cited by 169
Metadata
- Authors
- Jouseau, R., Salva, S., & Samir, C.
- Affiliations
- Université Clermont Auvergne (FR)
- Corresponding author
- Jouseau, Roxane
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2022
- Volume
- 13756
- Issue
- 4
- Pages
- 82-93
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4312677676
- Cited by
- 5
- References
- 12
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2022: 1
- FWCI
- 1.63
- Type
- conference paper
- Language
- en
- Open access
- green
- uca.hal.science
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- www.springer.com/tdm (vor)
- Locations
- Lecture notes in computer science (publishedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- data quality, data cleaning, data repair, classification performance, data preprocessing, classification models
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- CoChat. (2026). Citation check: On Studying the Effect of Data Quality on Classification Performances. https://app.cochat.ai/papers/10.1007/978-3-031-21753-1_9
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- Jouseau, R., Salva, S., & Samir, C. (2022). On Studying the Effect of Data Quality on Classification Performances. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-031-21753-1_9
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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.
