Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/ass086
Title: Simple tiered classifiers
Authors: Hall, P.
Xia, Y. 
Xue, J.-H.
Keywords: Classification
Linear discriminant analysis
Linear logistic regression
Support vector machine
Tiered classifier
Issue Date: Jun-2013
Citation: Hall, P., Xia, Y., Xue, J.-H. (2013-06). Simple tiered classifiers. Biometrika 100 (2) : 431-445. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/ass086
Abstract: In this paper we propose simple, general tiered classifiers for relatively complex data. Empirical studies on real and simulated data show that three two-tier classifiers, which are respective extensions of linear discriminant analysis, linear logistic regression and support vector machines, can reduce noticeably the relatively high misclassification error of their original single-tier counterparts, without significantly increasing computational labour. © 2013 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/105365
ISSN: 00063444
DOI: 10.1093/biomet/ass086
Appears in Collections:Staff Publications

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