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Classification with decision trees from a nonparametric predictive inference perspective

Abellán, J.; Baker, R.M.; Coolen, F.P.A.; Crossman, R.J.; Masegosa, A.R.

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Authors

J. Abellán

R.M. Baker

R.J. Crossman

A.R. Masegosa



Abstract

An application of nonparametric predictive inference for multinomial data (NPI) to classification tasks is presented. This model is applied to an established procedure for building classification trees using imprecise probabilities and uncertainty measures, thus far used only with the imprecise Dirichlet model (IDM), that is defined through the use of a parameter expressing previous knowledge. The accuracy of that procedure of classification has a significant dependence on the value of the parameter used when the IDM is applied. A detailed study involving 40 data sets shows that the procedure using the NPI model (which has no parameter dependence) obtains a better trade-off between accuracy and size of tree than does the procedure when the IDM is used, whatever the choice of parameter. In a bias-variance study of the errors, it is proved that the procedure with the NPI model has a lower variance than the one with the IDM, implying a lower level of over-fitting.

Journal Article Type Article
Publication Date Mar 1, 2014
Deposit Date Sep 13, 2013
Publicly Available Date Nov 28, 2014
Journal Computational Statistics & Data Analysis
Print ISSN 0167-9473
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 71
Pages 789-802
DOI https://doi.org/10.1016/j.csda.2013.02.009
Keywords Imprecise probabilities, Imprecise Dirichlet model, Nonparametric predictive inference model, Uncertainty measures, Supervised classification, Decision trees.
Public URL https://durham-repository.worktribe.com/output/1477979

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Accepted Journal Article (389 Kb)
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Copyright Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics & Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics & Data Analysis, 71, March 2014, 10.1016/j.csda.2013.02.009.






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