Abstract
During the last decade, data have played a key role for learning and decision making models. Unfortunately, the quality of data has been ignored or partially investigated as a pre-processing step. Motivated by applications in various fields, we propose to study data quality and its impact on the performance of several learning models. In this work, we first study the difficulty of repairing errors by introducing a list of elementary repairing tasks ranging from easy to complex with an increasing level. Then, we form categories from the state-of-the-art cleaning and repairing methods. We also investigate if it is always efficient to repair data. By including standard classifications models and public dataset, our work enables their use in different contexts and can be extended to other machine learning applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abedjan, Z., et al.: Detecting data errors: where are we and what needs to be done? Proc. VLDB Endowment 9(12), 993–1004 (2016)
Ataeyan, M., Daneshpour, N.: A novel data repairing approach based on constraints and ensemble learning. Expert Syst. Appl. 159, 113511 (2020)
Blake, C., Merz, C.: UCI repository of machine learning databases (1998)
Chu, Xu, I.F.I., Papotti, P.: Holistic data cleaning: putting violations into context. In: IEEE 29th International Conference on Data Engineering (ICDE) (2013)
Chu, X., Ilyas, I.F.: Qualitative data cleaning. Proc. VLDB Endowment 9(13), 1605–1608 (2016)
Chu, X., Ilyas, I.F., Krishnan, S., Wang, J.: Data cleaning: Overview and emerging challenges. In: SIGMOD Proceedings of the 2016 International Conference on Management of Data, pp. 2201–2206 (2016)
Chu, X., et al.: Katara: a data cleaning system powered by knowledge bases and crowdsourcing. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1247–1261 (2015)
Hynes, N., Sculley, D., Terry, M.: The Data Linter: Lightweight, Automated Sanity Checking for ML Data Sets (2017)
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)
Li, P., Rao, X., Blase, J., Zhang, Y., Chu, X., Zhang, C.: CleanML: a study for evaluating the impact of data cleaning on ml classification tasks. In: 36th IEEE International Conference on Data Engineering (ICDE 2020) (virtual) (2021)
Li, Y., Vasconcelos, N.: Repair: Removing representation bias by dataset resampling. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9572–9581 (2019)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Qahtan, A., Tang, N., Ouzzani, M., Cao, Y., Stonebraker, M.: Pattern functional dependencies for data cleaning. Proc. VLDB Endowment 13(5), 684–697 (2020)
Rekatsinas, T., Chu, X., Ilyas, I.F., Ré, C.: HoloClean: holistic data repairs with probabilistic inference. Proc. VLDB Endowment 10(11), 1190–1201 (2017)
Wu, R., Chaba, S., Sawlani, S., Chu, X., Thirumuruganathan, S.: Zeroer: entity resolution using zero labeled examples. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data, pp. 1149–1164 (2020)
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms (2017)
Zhang, X., Ji, Y., Nguyen, C., Wang, T.: DeepClean: data cleaning via question asking. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 283–292 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Jouseau, R., Salva, S., Samir, C. (2022). On Studying the Effect of Data Quality on Classification Performances. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-031-21753-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21752-4
Online ISBN: 978-3-031-21753-1
eBook Packages: Computer ScienceComputer Science (R0)