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Cited by 8,947

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Wrappers for feature subset selection

Open access
Kohavi, Ron, John, George H.·1997·Artificial Intelligence
DOI 10.1016/s0004-3702(97)00043-x
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Citations

Cites

Research works printed but not linked: 31

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  2. Aha, 1991, Instance-based learning algorithms, Machine Learning, 6, 37, 10.1007/BF00153759 · DOI
  3. Almuallim, 1994, Learning Boolean concepts in the presence of many irrelevant features, Artificial Intelligence, 69, 279, 10.1016/0004-3702(94)90084-1 · DOI
  4. Anderson, 1992, Explorations of an incremental, Bayesian algorithm for categorization, Machine Learning, 9, 275, 10.1007/BF00994109 · DOI
  5. Ben-Bassat, 1982, Use of distance measures, information measures and error bounds in feature evaluation, Vol. 2, 773
  6. Berliner, 1979, The B∗ tree search algorithm: a best-first proof procedure, Artificial Intelligence, 12, 23, 10.1016/0004-3702(79)90003-1 · DOI
  7. 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
  8. Buntine, 1992, Learning classification trees, Statist. and Comput., 2, 63, 10.1007/BF01889584 · DOI · Paper page
  9. 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
  10. De Mántaras, 1991, A distance-based attribute selection measure for decision tree induction, Machine Learning, 6, 81, 10.1023/A:1022694001379 · DOI · Paper page
  11. Furnival, 1974, Regression by leaps and bounds, Technometrics, 16, 499, 10.1080/00401706.1974.10489231 · DOI
  12. Gennari, 1989, Models of incremental concept formation, Artificial Intelligence, 40, 11, 10.1016/0004-3702(89)90046-5 · DOI · Paper page
  13. Hoeffding, 1963, Probability inequalities for sums of bounded random variables, J. Amer. Statist. Assoc., 58, 13, 10.1080/01621459.1963.10500830 · DOI · Paper page
  14. Judd, 1988, On the complexity of loading shallow neural networks, J. Complexity, 4, 177, 10.1016/0885-064X(88)90019-2 · DOI
  15. Kohavi, 1995, The power of decision tables, Vol. 914, 174
  16. Krogh, 1995, Neural network ensembles, cross validation, and active learning, Vol. 7
  17. Littlestone, 1994, The weighted majority algorithm, Inform. and Comput., 108, 212, 10.1006/inco.1994.1009 · DOI
  18. Mallows, 1973, Some comments on cp, Technometrics, 15, 661
  19. Marill, 1963, On the effectiveness of receptors in recognition systems, IEEE Trans. Inform. Theory, 9, 11, 10.1109/TIT.1963.1057810 · DOI · Paper page
  20. Maron, 1994, Hoeffding races: accelerating model selection search for classification and function approximation, Vol. 6
  21. Miller, 1984, Selection of subsets of regression variables, J. Roy. Statist. Soc. A, 147, 389, 10.2307/2981576 · DOI
  22. Moret, 1982, Decision trees and diagrams, ACM Comput. Surveys, 14, 593, 10.1145/356893.356898 · DOI
  23. Narendra, 1977, A branch and bound algorithm for feature subset selection, IEEE Trans. Comput., 26, 917, 10.1109/TC.1977.1674939 · DOI · Paper page
  24. Pawlak, 1993, Rough sets: present state and the future, Found. Comput. Decision Sci., 18, 157
  25. Provost, 1995, Inductive policy: the pragmatics of bias selection, Machine Learning, 20, 35, 10.1007/BF00993474 · DOI
  26. Rendell, 1990, Learning hard concepts through constructive induction: Framework and rationale, Comput. Intell., 6, 247, 10.1111/j.1467-8640.1990.tb00298.x · DOI
  27. Schaffer, 1993, Selecting a classification method by cross-validation, Machine Learning, 13, 135, 10.1007/BF00993106 · DOI · Paper page
  28. Siedlecki, 1988, On automatic feature selection, Internat. J. Pattern Recognition and Artificial Intelligence, 2, 197, 10.1142/S0218001488000145 · DOI
  29. Wolpert, 1992, On the connection between in-sample testing and generalization error, Complex Systems, 6, 47
  30. Yan, 1992, Stochastic discrete optimization, SIAM J. Control and Optimization, 30, 594, 10.1137/0330034 · DOI
  31. Yu, 1993, A more efficient branch and bound algorithm for feature selection, Pattern Recognition, 26, 883, 10.1016/0031-3203(93)90054-Z · DOI

Cited by

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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)
DOI
10.1016/s0004-3702(97)00043-x
OpenAlex
W2017337590
Cited by
8,947
References
10
FWCI
109.48
Type
journal article
Language
en
Open access
bronze
Licence

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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.