Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31159
Authors: 
Krause, Rüdiger
Tutz, Gerhard
Year of Publication: 
2004
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 390
Abstract: 
Gene expression datasets usually have thousends of explanatory variables which are observed on only few samples. Generally most variables of a dataset have no effect and one is interested in eliminating these irrelevant variables. In order to obtain a subset of relevant variables an appropriate selection procedure is necessary. In this paper we propose the selection of variables by use of genetic algorithms with the logistic regression as underlying modelling procedure. The selection procedure aims at minimizing information criteria like AIC or BIC. It is demonstrated that selection of variables by genetic algorithms yields models which compete well with the best available classification procedures in terms of test misclassification error.
Subjects: 
Genetic algorithm
Variable selection
Logistic regression
AIC
BIC
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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