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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Self-citationWolpert, D. H. (1992) · Neural Networks · doi:10.1016/s0893-6080(05)80023-1 · Cited by 7,951
The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578
Boosting a Weak Learning Algorithm by Majority
Freund, Y. (1995) · Information and Computation · doi:10.1006/inco.1995.1136 · Cited by 1,674
Constructing optimal binary decision trees is NP-complete
Hyafil, L., & Rivest, R. L. (1976) · Information Processing Letters · doi:10.1016/0020-0190(76)90095-8 · Cited by 959
Research works printed but not linked: 31
- Aha, 1992, Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms, Internat. J. Man-Machine Studies, 36, 267, 10.1016/0020-7373(92)90018-G · DOI · Paper page
- Aha, 1991, Instance-based learning algorithms, Machine Learning, 6, 37, 10.1007/BF00153759 · DOI · Paper page
- Almuallim, 1994, Learning Boolean concepts in the presence of many irrelevant features, Artificial Intelligence, 69, 279, 10.1016/0004-3702(94)90084-1 · DOI · Paper page
- Anderson, 1992, Explorations of an incremental, Bayesian algorithm for categorization, Machine Learning, 9, 275, 10.1007/BF00994109 · DOI
- Ben-Bassat, 1982, Use of distance measures, information measures and error bounds in feature evaluation, Vol. 2, 773
- Berliner, 1979, The B∗ tree search algorithm: a best-first proof procedure, Artificial Intelligence, 12, 23, 10.1016/0004-3702(79)90003-1 · DOI
- Blum, 1992, Training a 3-node neural network is NP-complete, Neural Networks, 5, 117, 10.1016/S0893-6080(05)80010-3 · DOI · Paper page
- Buntine, 1992, Learning classification trees, Statist. and Comput., 2, 63, 10.1007/BF01889584 · DOI · Paper page
- Cover, 1977, On the possible orderings in the measurement selection problem, IEEE Trans. Systems Man Cybernet., 7, 657, 10.1109/TSMC.1977.4309803 · DOI · Paper page
- De Mántaras, 1991, A distance-based attribute selection measure for decision tree induction, Machine Learning, 6, 81, 10.1023/A:1022694001379 · DOI · Paper page
- Furnival, 1974, Regression by leaps and bounds, Technometrics, 16, 499, 10.1080/00401706.1974.10489231 · DOI · Paper page
- Gennari, 1989, Models of incremental concept formation, Artificial Intelligence, 40, 11, 10.1016/0004-3702(89)90046-5 · DOI · Paper page
- Hoeffding, 1963, Probability inequalities for sums of bounded random variables, J. Amer. Statist. Assoc., 58, 13, 10.1080/01621459.1963.10500830 · DOI · Paper page
- Judd, 1988, On the complexity of loading shallow neural networks, J. Complexity, 4, 177, 10.1016/0885-064X(88)90019-2 · DOI
- Kohavi, 1995, The power of decision tables, Vol. 914, 174
- Krogh, 1995, Neural network ensembles, cross validation, and active learning, Vol. 7
- Littlestone, 1994, The weighted majority algorithm, Inform. and Comput., 108, 212, 10.1006/inco.1994.1009 · DOI · Paper page
- Mallows, 1973, Some comments on cp, Technometrics, 15, 661
- Marill, 1963, On the effectiveness of receptors in recognition systems, IEEE Trans. Inform. Theory, 9, 11, 10.1109/TIT.1963.1057810 · DOI · Paper page
- Maron, 1994, Hoeffding races: accelerating model selection search for classification and function approximation, Vol. 6
- Miller, 1984, Selection of subsets of regression variables, J. Roy. Statist. Soc. A, 147, 389, 10.2307/2981576 · DOI
- Moret, 1982, Decision trees and diagrams, ACM Comput. Surveys, 14, 593, 10.1145/356893.356898 · DOI
- Narendra, 1977, A branch and bound algorithm for feature subset selection, IEEE Trans. Comput., 26, 917, 10.1109/TC.1977.1674939 · DOI · Paper page
- Pawlak, 1993, Rough sets: present state and the future, Found. Comput. Decision Sci., 18, 157
- Provost, 1995, Inductive policy: the pragmatics of bias selection, Machine Learning, 20, 35, 10.1007/BF00993474 · DOI
- Rendell, 1990, Learning hard concepts through constructive induction: Framework and rationale, Comput. Intell., 6, 247, 10.1111/j.1467-8640.1990.tb00298.x · DOI
- Schaffer, 1993, Selecting a classification method by cross-validation, Machine Learning, 13, 135, 10.1007/BF00993106 · DOI · Paper page
- Siedlecki, 1988, On automatic feature selection, Internat. J. Pattern Recognition and Artificial Intelligence, 2, 197, 10.1142/S0218001488000145 · DOI · Paper page
- Wolpert, 1992, On the connection between in-sample testing and generalization error, Complex Systems, 6, 47
- Yan, 1992, Stochastic discrete optimization, SIAM J. Control and Optimization, 30, 594, 10.1137/0330034 · DOI
- Yu, 1993, A more efficient branch and bound algorithm for feature selection, Pattern Recognition, 26, 883, 10.1016/0031-3203(93)90054-Z · DOI
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Kukolja, D., Popović, S., Horvat, M., Kovač, B., & Ćosić, K. (2014) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2014.05.006 · Cited by 66
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Sharma, H., Drukker, L., Chatelain, P., Droste, R., Papageorghiou, A. T., & Noble, J. A. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101973 · Cited by 48
Pang, G., Cao, L., Chen, L., & Liu, H. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0052 · Cited by 43
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Malik, M. N., Azam, M. A., Ehatisham-Ul-Haq, M., Ejaz, W., & Khalid, A. (2019) · Sensors · doi:10.3390/s19112466 · Cited by 39
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Cai, L., Huang, S., Zhang, Y., Lu, J., & Zhang, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103631 · Cited by 33
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Ustun, B., Tracà, S., & Rudin, C. (2013) · arXiv · doi:10.48550/arxiv.1306.6677 · Cited by 26
Gao, Y., Xu, A., Hu, P. J.-H., & Cheng, T.-H. (2017) · Decision Support Systems · doi:10.1016/j.dss.2017.01.007 · Cited by 26
Bennett, C., Doub, T., Bragg, A., Luellen, J., Regenmorter, C. V., Lockman, J., & Reiserer, R. (2011) · 2011 IEEE First International Conference on Healthcare Informatics, Imaging and Systems Biology · doi:10.1109/hisb.2011.20 · Cited by 20
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Vishwanath, M., Cao, S., Dutt, N., Rahmani, A. M., Lim, M. M., & Cao, H. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107948 · Cited by 3
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AbdulAbbas, A. M., Alkanany, R., Al-Nuaimi, Y. A. K., & Al-Hamdawee, Z. M. A. (2025) · Engineering, Technology & Applied Science Research · doi:10.48084/etasr.14155 · Cited by 3
College of Engineering, P., Kamathe, R., Joshi, K., & P.E.S's Modern College of Engineering, I. (2017) · ICTACT Journal on Image and Video Processing · doi:10.21917/ijivp.2017.0219 · Cited by 1
Parsimonious statistical learning models for low flow estimation
Laimighofer, J., Melcher, M., & Laaha, G. (2021) · doi:10.5194/hess-2021-481 · Cited by 30
“, Lasso, boosting models, or treebased methods; (ii) filter-based methods (Guyon and Elisseeff, 2003), which include correlation-based variable selection or univariate regression filters; finally, (iii) wrapper-based methods (Kohavi and John, 1997) such as recursive feature elimination (RFE) (Guyon et al.”
Method
Metadata
- Authors
- Kohavi, R., & John, G. H.
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1997-12
- Volume
- 97
- Issue
- 1-2
- Pages
- 273-324
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W2017337590
- Cited by
- 8,947
- References
- 10
- FWCI
- 109.48
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (vor, 5715 d)
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 ›
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Data sources
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.