The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Classification Trees With Unbiased Multiway Splits
Abstract
Two univariate split methods and one linear combination split method are proposed for the construction of classification trees with multiway splits. Examples are given where the trees are more compact and hence easier to interpret than binary trees. A major strength of the univariate split methods is that they have negligible bias in variable selection, both when the variables differ in the number of splits they offer and when they differ in the number of missing values. This is an advantage because inferences from the tree structures can be adversely affected by selection bias. The new methods are shown to be highly competitive in terms of computational speed and classification accuracy of future observations.
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Cites
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Cited by
Biau, G., & Scornet, E. (2016) · Test · doi:10.1007/s11749-016-0481-7 · Cited by 4,149
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
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Self-citation
Metadata
- Authors
- Kim, H., & Loh, W.-Y.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2001-06
- Volume
- 96
- Issue
- 454
- Pages
- 589-604
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2072154218
- Cited by
- 391
- References
- 33
- FWCI
- 32.01
- Type
- journal article
- Language
- en
- Open access
- closed
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