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Generalised indirect classifiers

Peters, A and Hothorn, T and Lausen, B (2005) 'Generalised indirect classifiers.' Computational Statistics and Data Analysis, 49 (3). 849 - 861. ISSN 0167-9473

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Abstract

Supervised classifiers are usually based on a set of predictors given in the learning sample as well as in later test samples. Especially in the medical field a reduction of the number of examinations is often desired to save patients time and costs. The approach of indirect classification makes use of all available variables of the learning sample, although it classifies based only on a reduced set of variables. A general definition of indirect classification is given and a specific generalised indirect classifier is proposed. This classifier combines an arbitrary number of regression models which predict those variables that are not acquired for future observations. The performance of the generalised indirect classifier is investigated by using a simulation model which mimics different kinds of decision surfaces and by the application to different data sets. Misclassification results of direct and indirect classifiers are compared. © 2004 Elsevier B.V. All rights reserved.

Item Type: Article
Subjects: H Social Sciences > HA Statistics
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Berthold Lausen
Date Deposited: 30 May 2012 18:45
Last Modified: 30 Jan 2019 16:21
URI: http://repository.essex.ac.uk/id/eprint/2462

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