The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A method of choosing multiway partitions for classification and decision trees
Abstract
A method is given of choosing k-way partitions (where 2 ≤ k ≤ (number of categories of predictor variable)) in classification or decision tree analyses. The method, like that proposed by Kass, chooses the best partition on the basis of statistical significanceand uses the Bonferroni inequality to calculate the significance. Unlike Kass's algorithm, the algorithm does not favour simple partitions (low values of k) nor does it discriminate against free-type (no restriction on order of values) predictor variables with many categories. A method of adjusting the significance for the number of predictor variables and of using multiple comparisons to put an upper bound on the significance is given. Monte Carlo tests show that the algorithm gives slightly conservative tests of significance for both small and large samples and does not favour one type of predictor variable over another. The algorithm is incoroporated in a PC software program, Knowledgeseeker,which is briefly described.
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Citations
Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
An exploratory technique for investigating large quantities of categorical data
Kass, G. V. (1980) · Applied Statistics · doi:10.2307/2986296 · Cited by 2,880
Alchemy in the behavioral sciences
Einhorn, H. J. (1972) · Public Opinion Quarterly · doi:10.1086/268019 · Cited by 65
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Metadata
- Authors
- Biggs, D., De Ville, B., & Suen, E.
- Venue
- Journal of Applied Statistics
- Publisher
- Informa UK Limited
- Published
- 1991-01
- Volume
- 18
- Issue
- 1
- Pages
- 49-62
- ISSN
- 0266-4763 (ISSN-L)
- 1360-0532
- OpenAlex
- W1966602182
- Cited by
- 574
- References
- 3
- FWCI
- 15.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Biggs, D., De Ville, B., & Suen, E. (1991). A method of choosing multiway partitions for classification and decision trees. Journal of Applied Statistics. https://doi.org/10.1080/02664769100000005
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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.
