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Ensemble classification of paired data

Adler, W and Brenning, A and Potapov, S and Schmid, M and Lausen, B (2011) 'Ensemble classification of paired data.' Computational Statistics and Data Analysis, 55 (5). 1933 - 1941. ISSN 0167-9473

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Abstract

In many medical applications, data are taken from paired organs or from repeated measurements of the same organ or subject. Subject based as opposed to observation based evaluation of these data results in increased efficiency of the estimation of the misclassification rate. A subject based approach for classification in the generation of bootstrap samples of bagging and bundling methods is analyzed. A simulation model is used to compare the performance of different strategies to create the bootstrap samples which are used to grow individual trees. The proposed approach is compared to linear discriminant analysis, logistic regression, random forests and gradient boosting. Finally, the simulation results are applied to glaucoma diagnosis using both eyes of glaucoma patients and healthy controls. It is demonstrated that the proposed subject based resampling reduces the misclassification rate. © 2010 Elsevier B.V. All rights reserved.

Item Type: Article
Subjects: Q Science > QA Mathematics
R Medicine > R Medicine (General)
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 09 Dec 2011 23:35
Last Modified: 17 Aug 2017 18:14
URI: http://repository.essex.ac.uk/id/eprint/1769

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