The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Unbiased split selection for classification trees based on the Gini Index
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
Research works printed but not linked: 16
- Benjamini, 1995, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. Roy. Statist. Soc. B, 57, 289, 10.1111/j.2517-6161.1995.tb02031.x · DOI · Paper page
- Boulesteix, 2006, Maximally selected chi-square statistics and binary splits of nominal variables, Biometrical J., 48, 838, 10.1002/bimj.200510191 · doi:10.1002/bimj.200510191
- Boulesteix, 2006, Maximally selected chi-square statistics for ordinal variables, Biometrical J., 48, 451, 10.1002/bimj.200510161 · doi:10.1002/bimj.200510161
- Boulesteix, 2006, Identification of interaction patterns and classification with applications to microarray data, Comput. Statist. Data Anal., 50, 783, 10.1016/j.csda.2004.10.004 · doi:10.1016/j.csda.2004.10.004
- Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324 · DOI · Paper page
- Kim, 2001, Classification trees with unbiased multiway splits, J. Amer. Statist. Assoc., 96, 589, 10.1198/016214501753168271 · DOI · Paper page
- Koziol, 1991, On maximally selected chi-square statistics, Biometrics, 47, 1557, 10.2307/2532406 · DOI
- Loh, 2002, Regression trees with unbiased variable selection and interaction detection, Statist. Sinica, 12, 361
- Loh, 1997, Split selection methods for classification trees, Statist. Sinica, 7, 815
- Miller, 1982, Maximally selected Chi square statistics, Biometrics, 38, 1011, 10.2307/2529881 · DOI · Paper page
- Schmaußer, M., 2005. Auswirkungen verschiedener Stoffwechsellagen auf die Fertilität beim Milchrind unter besonderer Berücksichtigung der individuellen Futteraufnahme und unter Berücksichtigung verschiedener Melksysteme. Ph.D. Thesis, Faculty of Veterinary Medicine, University of Munich LMU, Munich, Germany.
- Shih, 2004, A note on split selection bias in classification trees, Comput. Statist. Data Anal., 45, 457, 10.1016/S0167-9473(03)00064-1 · DOI
- Shih, 2004, Variable selection bias in regression trees with constant fits, Comput. Statist. Data Anal., 45, 595, 10.1016/S0167-9473(03)00036-7 · DOI
- Strobl, C., 2005. Variable selection in classification trees based on imprecise probabilities. In: Cozman, F., Nau, R., Seidenfeld, T. (Eds.), Proceedings of the Fourth International Symposium on Imprecise Probabilities and their Applications, Carnegy Mellon University, Pittsburgh, PA, USA, SIPTA, Manno, pp. 340–348.
- Strobl, C., Boulesteix, A.-L., Zeileis, A., Hothorn, T., 2006. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics, accepted for publication. · DOI · Paper page
- White, 1994, Bias in information based measures in decision tree induction, Mach. Learn., 15, 321, 10.1007/BF00993349 · DOI · Paper page
Cited by
Strobl, C., Malley, J., & Tutz, G. (2009) · Psychological Methods · doi:10.1037/a0016973 · Cited by 2,583
Self-citationEmpirical characterization of random forest variable importance measures
Archer, K. J., & Kimes, R. V. (2008) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.08.015 · Cited by 1,123
Fifty Years of Classification and Regression Trees
Loh, W. (2014) · International Statistical Review · doi:10.1111/insr.12016 · Cited by 649
A new variable selection approach using Random Forests
Hapfelmeier, A., & Ulm, K. (2013) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2012.09.020 · Cited by 259
The accuracy of passive phone sensors in predicting daily mood
Pratap, A., Atkins, D. C., Renn, B. N., Tanana, M. J., Mooney, S. D., Anguera, J. A., & Areán, P. A. (2019) · Depression and Anxiety · doi:10.1002/da.22822 · Cited by 119
Metadata
- Authors
- Strobl, C., Boulesteix, A.-L., & Augustin, T.
- Venue
- Computational Statistics & Data Analysis
- Publisher
- Elsevier BV
- Published
- 2007-09
- Volume
- 52
- Issue
- 1
- Pages
- 483-501
- ISSN
- 0167-9473 (ISSN-L)
- OpenAlex
- W2110878112
- Cited by
- 308
- References
- 3
- FWCI
- 24.93
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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- Strobl, C., Boulesteix, A.-L., & Augustin, T. (2007). Unbiased split selection for classification trees based on the Gini Index. Computational Statistics & Data Analysis. https://doi.org/10.1016/j.csda.2006.12.030
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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.
