Authors: Szepannek, Gero
Weihs, Claus
Title: Variable selection for discrimination of more than two classes where data are sparse
Language (ISO): en
Abstract: In classification, with an increasing number of variables, the required number of observations grows drastically. In this paper we present an approach to put into effect the maximal possible variable selection, by splitting a K class classification problem into pairwise problems. The principle makes use of the possibility that a variable that discriminates two classes will not necessarily do so for all such class pairs. We further present the construction of a classification rule based on the pairwise solutions by the Pairwise Coupling algorithm according to Hastie and Tibshirani (1998). The suggested proceedure can be applied to any classification method. Finally, situations with lack of data in multidimensional spaces are investigated on different simulated data sets to illustrate the problem and the possible gain. The principle is compared to the classical approach of linear and quadratic discriminant analysis.
Subject Headings: Classification
discriminant analysis
Hastie
lack of data
Pairwise Coupling algorithm
Tibshirani
URI: http://hdl.handle.net/2003/21641
http://dx.doi.org/10.17877/DE290R-14504
Issue Date: 2005-10-11T14:38:18Z
Appears in Collections:Sonderforschungsbereich (SFB) 475

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