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Author Krause, Rüdiger ♦ Tutz, Gerhard
Source EconStor
Content type Text
Publisher Techn. Univ.; Sonderforschungsbereich 386, Statistische Analyse Diskreter Strukturen cMünchen
File Format PDF ♦ PS / EPS
Language English
Subject Domain (in DDC) Social sciences ♦ Collections of general statistics
Subject Keyword Genetic algorithm ♦ Variable selection ♦ Logistic regression ♦ AIC ♦ BIC
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.
Part of series Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München x390
Learning Resource Type Article
Publisher Date 2004-01-01
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