The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Overfitting Avoidance as Bias
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“The most common interpretation of an Occamist bias is that amongst all hypotheses consistent with the data, the simplest is the most likely 28 .”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Schaffer, 1993
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Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Classification And Regression Trees
Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417
Practical Nonparametric Statistics
Groggel, D. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271101 · Cited by 11,076
Applied Nonparametric Statistics
Wozniak, P. J., & Daniel, W. W. (1991) · Technometrics · doi:10.2307/1268795 · Cited by 5,008
Valiant, L. G. (1984) · ACM eBooks · doi:10.1145/3749740.3749746 · Cited by 4,211
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
(2013) · Springer eBooks · Cited by 2,625
Quinlan, J. R. (1987) · International Journal of Man-Machine Studies · doi:10.1016/s0020-7373(87)80053-6 · Cited by 2,553
Flagged first, then by citations
Research works printed but not linked: 2
- Breiman, L., Friedman, J., Olshen, R., amp; Stone, C. (1984). Classification andRegression Trees. Pacific Grove,CA: Wadsworth amp; Brooks.
- Holte, R.C. (1991). Very simple classification rules perform well on most datasets(Technical Report TR-91-16).Ottawa, Canada: University of Ottawa, Department of Computer Science.
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Items 301–310 of 310
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.002.0003
Kişi, Ö., Melo, C. G. L. E., Fischer, L., Brandt, H., Heims, L., Loaec, A., Masomy, W., Mohammed, P., Roehl, J., & Külls, C. (2024) · Environmental Science and Pollution Research · doi:10.1007/s11356-024-33939-x
Shin, E. K. (2024) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780197653609.013.20
On the Performance and Robustness of Linear Model U-Trees in Mimic Learning
Green, M., & Sheppard, J. W. (2024) · International Conference on Machine Learning and Applications · doi:10.1109/icmla61862.2024.00027
Dynamics of Inequality of Opportunity in Wage Earnings in India: A Machine Learning Approach.Preprint
Panampilly, G., & Ranganathan, T. (2025) · Research Square · doi:10.21203/rs.3.rs-7899742/v1
Conclusions and Research Directions
Freitas, A. A. (2002) · Natural computing series · doi:10.1007/978-3-662-04923-5_12
Burbrink, C. M., Dickson, K. H., Fidan, E. N., Walters, K. J., & Sorochan, J. C. (2026) · Sports Engineering · doi:10.1007/s12283-026-00540-z
Maximum likelihood estimation and rare events
Marwala, T. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36322-1.00026-2
Data Mining Tasks and Concepts
Freitas, A. A. (2002) · Natural computing series · doi:10.1007/978-3-662-04923-5_2
Metadata
- Authors
- Schaffer, C.
- Affiliations
- City University of New York (US)
- Corresponding author
- Schaffer, Cullen
- Venue
- Machine Learning
- Published
- 1993-02
- Volume
- 10
- Issue
- 2
- Pages
- 153-178
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1995023359
- Cited by
- 310
- References
- 29
- Citations by year
- 2026: 2 · 2025: 7 · 2024: 10 · 2023: 12 · 2022: 12 · 2021: 29 · 2020: 19 · 2019: 11 · 2018: 6 · 2017: 7 · 2016: 5 · 2015: 3 · 2014: 2 · 2013: 1 · 2012: 5
- FWCI
- 62.52
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- overfitting avoidance, decision tree induction, selective pruning, bias-variance trade-off, prediction accuracy, generalization
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