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dc.contributor.authorKrause, Rüdigeren_US
dc.contributor.authorTutz, Gerharden_US
dc.description.abstractGene 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.en_US
dc.publisherTechn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen Münchenen_US
dc.relation.ispartofseriesDiscussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 390en_US
dc.subject.keywordGenetic algorithmen_US
dc.subject.keywordVariable selectionen_US
dc.subject.keywordLogistic regressionen_US
dc.titleVariable selection and discrimination in gene expression data by genetic algorithmsen_US
dc.typeWorking Paperen_US
Appears in Collections:Discussion papers, SFB 386, LMU München

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