The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Discretization for naive-Bayes learning: managing discretization bias and variance
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Cites
Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings
Friedman, M. (1940) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177731944 · Cited by 2,785
Automatic Indexing: An Experimental Inquiry
Maron, M. E. (1961) · Journal of the ACM · doi:10.1145/321075.321084 · Cited by 570 · cited 1×
“They have long been a core technique in information retrieval (Maron and Kuhns, 1960; Maron, 1961; Lewis, 1992; Guthrie and Walker, 1994; Lewis and Gale, 1994; Kalt, 1996; Larkey and Croft, 1996; Pazzani et al., 1996; Starr et al., 1996a; Joachims, 1997; Koller and Sahami, 1997; Li and Yamanishi, 1997; Mitchell, 1997;…”
MethodAndroutsopoulos, I., Koutsias, J., Chandrinos, K. V., & Spyropoulos, C. D. (2000) · Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/345508.345569 · Cited by 465
Cited by
Big data preprocessing: methods and prospects
García, S., Ramírez-Gallego, S., Luengo, J., Benítez, J. M., & Herrera, F. (2016) · Big Data Analytics · doi:10.1186/s41044-016-0014-0 · Cited by 709
Not So Naive Bayes: Aggregating One-Dependence Estimators
Webb, G. I., Boughton, J. R., & Wang, Z. (2005) · Machine Learning · doi:10.1007/s10994-005-4258-6 · Cited by 708
Self-citationSelf-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
Triguero, I., García, S., & Herrera, F. (2015) · Knowledge and Information Systems · doi:10.1007/s10115-013-0706-y · Cited by 573
A Survey of Discretization Techniques: Taxonomy and Empirical Analysis in Supervised Learning
Garcia, S., Luengo, J., Sáez, J. A., López, V., & Herrera, F. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.35 · Cited by 548
A survey on data preprocessing for data stream mining: Current status and future directions
Ramírez-Gallego, S., Krawczyk, B., García, S., Woźniak, M., & Herrera, F. (2017) · Neurocomputing · doi:10.1016/j.neucom.2017.01.078 · Cited by 502
Metadata
- Authors
- Yang, Y., & Webb, G. I.
- Venue
- Machine Learning
- Published
- 2008-09-04
- Volume
- 74
- Issue
- 1
- Pages
- 39-74
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2113001205
- Cited by
- 250
- References
- 4
- FWCI
- 16.96
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Yang, Y., & Webb, G. I. (2009). Discretization for naive-Bayes learning: managing discretization bias and variance. Machine Learning. https://doi.org/10.1007/s10994-008-5083-5
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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.
