The DOI matches its OpenAlex record; 32 of the 32 references it cites were found in a registry or an index.
Data Preprocessing For Supervised Leaning
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Citations
Cites
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
(2000) · CrossRef Listing of Deleted DOIs · doi:10.1162/153244303322753616 · Cited by 3,502
Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning
Fayyad, U. M., & Irani, K. B. (1993) · International Joint Conference on Artificial Intelligence · Cited by 2,870
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Hall, M. A. (2000) · Cited by 1,715
Data preparation for data mining
Pyle, D. (1999) · Cited by 1,368
Feature subset selection using a genetic algorithm
Yang, J., & Honavar, V. (1998) · IEEE Intelligent Systems · doi:10.1109/5254.671091 · Cited by 1,362
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Hernández, M. A., & Stolfo, S. J. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009761603038 · Cited by 812
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Kononenko, I., Šimec, E., & Robnik-Šikonja, M. (1997) · Applied Intelligence · doi:10.1023/a:1008280620621 · Cited by 796
Identifying Mislabeled Training Data
Brodley, C. E., & Friedl, M. A. (1999) · Cited by 718
Selection of Relevant Features in Machine Learning.
Langley, P. (1994) · doi:10.21236/ada292575 · Cited by 709
Cited by
Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.03.004 · Cited by 527
Deng, Z., Hu, X., Lin, X., Che, Y., Xu, L., & Guo, W. (2020) · Energy · doi:10.1016/j.energy.2020.118000 · Cited by 423
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
Ashmore, R., Calinescu, R., & Paterson, C. (2022) · ACM Computing Surveys · doi:10.1145/3453444 · Cited by 256
Tougui, I., Jilbab, A., & Mhamdi, J. E. (2021) · Healthcare Informatics Research · doi:10.4258/hir.2021.27.3.189 · Cited by 199
Contextual anomaly detection framework for big sensor data
Hayes, M. A., & Capretz, M. A. (2015) · Journal of Big Data · doi:10.1186/s40537-014-0011-y · Cited by 149
Metadata
- Authors
- Kotsiantis, S. B., Kanellopoulos, D. N., & Pintelas, P.
- Venue
- Zenodo (CERN European Organization for Nuclear Research)
- Publisher
- European Organization for Nuclear Research
- Published
- 2007-12-28
- Volume
- 1
- Issue
- 12
- Pages
- 4104-4109
- OpenAlex
- W1961147827
- Cited by
- 631
- References
- 32
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Data Preprocessing For Supervised Leaning. https://app.cochat.ai/papers/10.5281/zenodo.1082414
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- Kotsiantis, S. B., Kanellopoulos, D. N., & Pintelas, P. (2007). Data Preprocessing For Supervised Leaning. Zenodo (CERN European Organization for Nuclear Research). https://doi.org/10.5281/zenodo.1082414
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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.
