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
Open accessOverview
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measured
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
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.
Cited by
Sorted by citations
Items 201–300 of 310
Computational Properties of Probabilistic Neural Networks
Grim, J., & Hora, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4_4 · Cited by 6
A COMMON ALGEBRAIC FRAMEWORK OF EMPIRICAL LEARNING METHODS BASED ON ROUGH SETS AND MATROID THEORY
Tsumoto, S., & Tanaka, H. (1996) · Fundamenta Informaticae · doi:10.3233/fi-1996-272313 · Cited by 6
Preventing Overfitting in Learning Text Patterns for Document Categorization
Junker, M., & Dengel, A. R. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44732-6_14 · Cited by 6
Pattern Analysis and Classification
Coomans, D. H., & de Vel, O. Y. (1995) · The handbook of environmental chemistry · doi:10.1007/978-3-540-49148-4_9 · Cited by 6
Responsible guidelines for authorship attribution tasks in NLP
Saxena, V., Tamò‐Larrieux, A., DIJCK, G. V., & Spanakis, G. (2025) · Ethics and Information Technology · doi:10.1007/s10676-025-09821-w · Cited by 7
A Hybrid Rule-Induction/Likelihood-Ratio Based Approach for Predicting Protein-Protein Interactions
Iqbal, M., Freitas, A. A., & Johnson, C. G. (2009) · Intelligent systems reference library · doi:10.1007/978-3-642-01799-5_19 · Cited by 5
Structure and Majority Classes in Decision Tree Learning
Hickey, R. J. (2007) · doi:10.5555/1314498.1314557 · Cited by 5
The Need for Interpretability Biases
Fürnkranz, J., & Kliegr, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01768-2_2 · Cited by 5
A New Approach for Handling Classification Problems Based on Fuzzy Information Gain Measures
Shie, J.-D., & Chen, S. (2006) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzzy.2006.1681823 · Cited by 5
DCG Induction Using MDL and Parsed Corpora
Osborne, M. (2000) · Lecture notes in computer science · doi:10.1007/3-540-40030-3_12 · Cited by 5
Hoeffding Races--model selection for MRI classification
Maron, O. (1994) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 5
Fakhrusy, M., & Rosalia, C. A. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202566401014 · Cited by 4
Hao, Z., Yang, B., Ruggiano, N., Ma, Y., Guo, Y., & Pan, X. (2020) · The British Journal of Social Work · doi:10.1093/bjsw/bcaa237 · Cited by 4
Avoiding data over fitting in scientific discovery: experiments in functional genomics
Gamberger, D., & Nada Lavrač, A. (2004) · European Conference on Artificial Intelligence · Cited by 4
Making lexical sense of Japanese-English machine translation : A disambiguation extravaganza
Baldwin, T. (2001) · doi:10.11501/3191666 · Cited by 4
Interpreting Knowledge Representations in BP-SOM
Weijters, T., & van den Bosch, A. P. J. (1999) · Behaviormetrika · doi:10.2333/bhmk.26.107 · Cited by 4
Individual identification for different age groups using functional connectivity strength
Zhang, Y., Liu, S., & Yu, X. (2019) · Neurological Sciences · doi:10.1007/s10072-019-04109-6 · Cited by 3
Non Linear Fitting Methods for Machine Learning
Martínez‐García, E. A., Rodríguez, N. Á., Jorge, R. R., Mizera–Pietraszko, J., Sheba, J. K., Elara, M. R., & Magid, E. (2017) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-319-69835-9_76 · Cited by 3
Adnan, R. M., Liang, Z., & Kişi, Ö. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.139486 · Cited by 3
Transfer learning by centroid pivoted mapping in noisy environment
Thach, N. H., Tong, B., Shao, H., & Suzuki, E. (2012) · Journal of Intelligent Information Systems · doi:10.1007/s10844-012-0226-3 · Cited by 3
Glavaški, M., & Velicki, L. (2022) · Croatian Medical Journal · doi:10.3325/cmj.2022.63.370 · Cited by 3
Andersen, T., & Martinez, T. R. (2003) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.1999.832612 · Cited by 3
The low prediction accuracy problem in learning
Dai, H. (2002) · Australian and New Zealand Intelligent Information Systems Conference (ANZIIS) · doi:10.1109/anziis.1994.396990 · Cited by 3
On the Metric-based Approach to Supervised Concept Learning
Lavesson, N. (2008) · Cited by 3
KDB2000: An integrated knowledge discovery tool
Appice, A., Ceci, M., & Malerba, D. (2002) · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · doi:10.2495/data020511 · Cited by 3
Fang, J., Li, Y., & Shi, L. (2025) · MedMat. · doi:10.1097/mm9.0000000000000027 · Cited by 2
Aoyagi, M., Naito, K., Sato, Y., Kobayashi, A., Sakamoto, M., & Tumilty, S. (2022) · Journal of Manual & Manipulative Therapy · doi:10.1080/10669817.2022.2056310 · Cited by 2
Exploiting Machine Learning Bias: Predicting Medical Denials
Russell, S., Suros, F. M., & Kumar, A. (2024) · Proceedings of the AAAI Symposium Series · doi:10.1609/aaaiss.v3i1.31181 · Cited by 2
Hu, M., & Hu, Y.-H. F. (2021) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci54926.2021.00016 · Cited by 2
Contextual Decision Making in General Game Playing
Sheng, X., & Thuente, D. J. (2011) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2011.108 · Cited by 2
Robust Models in Information Retrieval
Lipka, N., & Stein, B. (2011) · International Workshop on Database and Expert Systems Applications (DEXA) · doi:10.1109/dexa.2011.73 · Cited by 2
Evaluating the effects of distance metrics on a NGE-based system
Figueira, L. B., & Nicolefti, M. D. C. (2005) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2004.1400867 · Cited by 2
Pruning strategies for nearest neighbor competence preservation learners
Angiulli, F., & Narváez, M. E. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.04.017 · Cited by 2
Feature Extraction Methods and Classification for Malware Incident News
Gumilar, G., Budiarto, E., Galinium, M., & Lim, C. (2023) · IEEE International Conference on Cryptography, Informatics, and Cybersecurity (ICoCICs) · doi:10.1109/icocics58778.2023.10276685 · Cited by 2
A Heuristic-Based Decision Tree Induction Method for Noisy Data
Kerdprasop, N., & Kerdprasop, K. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-27157-1_1 · Cited by 2
Suzuki, E. (2004) · Applied Intelligence · doi:10.1023/b:apin.0000047381.08666.c9 · Cited by 2
Discrete decision tree induction to avoid overfitting on categorical data
Kerdprasop, N., & Kerdprasop, K. (2011) · International Conference on Mathematical methods, Computational techniques and Intelligent systems · Cited by 2
Breast Cancer Risk Prediction Using Ions Motion Optimization Algorithm
Yang, C., Wu, K., & Chuang, L. (2016) · Journal of Life Sciences and Technologies · doi:10.18178/jolst.4.2.49-55 · Cited by 2
Worst-Case Analysis of Rule Discovery
Suzuki, E. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45650-3_31 · Cited by 2
Optimization of composite structures by estimation of distribution algorithms
Grosset, L., & Haftka, R. T. (2004) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 2
On the use of multi-objective evolutionary algorithms for classification rule induction
Setzkorn, C. (2005) · OpenGrey (Institut de l'Information Scientifique et Technique) · doi:10.17638/03186718 · Cited by 2
THEORETICAL NOTES How Persuasive Is a Good Fit? A Comment on Theory Testing
Roberts, S., & Pashler, H. (2000) · Cited by 2
Evaluation of Supervised Machine Learning for Classifying Video Traffic
Taylor, F. R. (2016) · NSUWorks (Nova Southeastern University) · Cited by 2
Tsumoto, S., & Tanaka, H. (1995) · Cited by 2
Marwala, T. (2024) · Springer eBooks · doi:10.1007/978-981-97-9251-1_5 · Cited by 1
Falco, P. D., & Delgado, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129313 · Cited by 1
Cuestiones éticas sobre la implantación de la inteligencia artificial en la administración pública
Pérez, P. J. B. (2023) · Revista Canaria de Administración Pública · doi:10.36151/rcap.2023.8 · Cited by 1
The Development of Discrete Decision Tree Induction for Categorical Data
Kerdprasop, N., & Kerdprasop, K. (2011) · Cited by 1
Interpretable neural networks with BP-SOM
Weijters, T., & van den Bosch, A. P. J. (1998) · Lecture notes in computer science · doi:10.1007/3-540-64574-8_442 · Cited by 1
Image classification optimization and results analysis based on VGG network
Shi, J., & Zhao, Y. (2020) · IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA) · doi:10.1109/iciba50161.2020.9277329 · Cited by 1
Sonar Inspired Optimization based Feature Selection
Tzanetos, A., & Dounias, G. D. (2020) · Hellenic Conference on Artificial Intelligence · doi:10.1145/3411408.3411438 · Cited by 1
Orchid: Building Dynamic Test Oracles with Training Bias for Improving Deep Neural Network Models
Wang, H., & Chan, W. K. (2021) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa52907.2021.00022 · Cited by 1
Liu, H., GEGOV, A. E., & Cocea, M. (2015) · Studies in big data · doi:10.1007/978-3-319-23696-4_1 · Cited by 1
Tsumoto, S., & Tanaka, H. (1994) · Cited by 1
A Bayesian Analysis of Algorithms for Learning Finite Functions
Cussens, J. (1995) · Elsevier eBooks · doi:10.1016/b978-1-55860-377-6.50026-8 · Cited by 1
Craddock, A. J. (1992) · National Conference on Artificial Intelligence · Cited by 1
Learning and revising theories in noisy domains
Rang, X., & Numao, M. (1997) · Lecture notes in computer science · doi:10.1007/3-540-63577-7_53 · Cited by 1
Knowledge evaluation: statistical evaluations
Jensen, D. D. (2002) · Oxford University Press eBooks · Cited by 1
Rule based systems for classification in machine learning context
Liu, H. (2015) · Cited by 1
Engster, D. (2011) · doi:10.53846/goediss-2914 · Cited by 1
Régression régularisée par contrainte de variabilité
Naji, Y., Brusquet, L. L., & Fleury, G. A. (2003) · Cited by 1
Freitas, A. A. (2002) · Natural computing series · doi:10.1007/978-3-662-04923-5_3
Tsumoto, S., Hirano, S., & Abe, H. (2010) · IFIP International Federation for Information Processing/IFIP · doi:10.1007/978-3-642-16327-2_19
Development of Methods How to Avoid the Overfitting-Effect within the GeLog-System
Kókai, G. (2003) · Lecture notes in computer science · doi:10.1007/978-3-540-45226-3_131
Spears, W. M., & Gordon, D. F. (1994)
Dai, H. (2012) · Springer eBooks · doi:10.1007/978-1-4614-1903-7_5
Liu, X., & Pedrycz, W. (2009) · Studies in fuzziness and soft computing · doi:10.1007/978-3-642-00402-5_10
Discovering Knowledge Nuggets with a Genetic Algorithm
Noda, E., & Freitas, A. A. (2006) · Springer eBooks · doi:10.1007/0-387-34296-6_12
On Interference of Signals and Generalization in Feedforward Neural NetworksPreprint
Rataj, A. (2003) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0310009
A QUASILINEAR CLASSIFIER FOR COMPLETE SEPARATION OF A DICHOTOMY
Prabhu, N. (2003) · International Journal of Artificial Intelligence Tools · doi:10.1142/s0218213003001332
BEXA: set covering vs. neural network knowledge acquisition-a comparative review
Cloete, I. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1997.614703
Application of Ant Colony Algorithm for Classification and Rule Generation of Data
Tiwari, P., & Verma, B. (2012) · Studies in computational intelligence · doi:10.1007/978-3-642-25507-6_14
How Persuasive Is THEORETICAL NOTES a Good Fit? A Comment on Theory Testing
Roberts, S., & Pashler, H. (2000)
Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens.
Cox, L. A. (1999) · International Conference on Artificial Intelligence and Statistics
Use of Attribute Selection Criteria in Decision Trees in Uncertain Domains
Crémilleux, B., & Robert, C. (2000) · Advances in fuzzy systems · doi:10.1142/9789812792563_0012
A New Training Set-Based Regularization For Regression Techniques
Naji, Y., Brusquet, L. L., & Fleury, G. A. (2004) · doi:10.5281/zenodo.38380
Performance testing of automated modeling for industrial applications
Sherry, D., & Schmidt, M. (2017) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3067695.3082534
Domingos, P. (2002)
Innovative and conventional modelling for avian acoustic and fire severity analyses
Tseng, Y.-C. (2019) · cIRcle (University of British Columbia) · doi:10.14288/1.0385980
A Multi-hop Localization Through Model Selection for Irregular Networks
Dou, R., Fang, X., Liu, Y., & Mo, X. (2020)
Dotan, R. (2021) · eScholarship (California Digital Library)
Overfitting-based Bias-generating Neural-network : Simulation of Pavlov’s Dog
Pan, J., Yoshikawa, A., & Yamamura, M. (2021) · International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) · doi:10.1109/isaiam53259.2021.00023
Choice, Selection, and Learning
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.003.0002
Modern Mendels and Organizational Adaptation
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.003.0007
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.003.0001
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.003.0006
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.003.0004
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.003.0005
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.003.0003
Pan, J., Yoshikawa, A., & Yamamura, M. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/4010945
Ahn, H., Choi, M. H., Jang, H., Jeong, S. H., & Park, Q. (2020) · doi:10.2196/preprints.26761
Fehérváry, I., & Kiss, T. (2020) · Tájökológiai Lapok · doi:10.56617/tl.3490
Tudor, J. (2023) · Cronfa (Swansea University) · doi:10.23889/suthesis.64642
Sampling Methods in Cybernetical Intelligence
Wong, K. K. L. (2023) · doi:10.1002/9781394217519.ch14
Levinthal, D. A. (2021) · doi:10.1093/oso/9780199684946.002.0006
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
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