caa a22 4500 378925881 CHVBK 20180305123617.0 cr unu---uuuuu 161128e20040609xx s 000 0 eng 10.2202/1544-6115.1054 doi (NATIONALLICENCE)gruyter-10.2202/1544-6115.1054 Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection [Elektronische Daten] [Minhui Paik, Yuhong Yang] Various discriminant methods have been applied for classification of tumors based on gene expression profiles, among which the nearest neighbor (NN) method has been reported to perform relatively well. Usually cross-validation (CV) is used to select the neighbor size as well as the number of variables for the NN method. However, CV can perform poorly when there is considerable uncertainty in choosing the best candidate classifier. As an alternative to selecting a single "winner," we propose a weighting method to combine the multiple NN rules. Four gene expression data sets are used to compare its performance with CV methods. The results show that when the CV selection is unstable, the combined classifier performs much better. ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston Multivariate Analysis nationallicence combining classifiers nationallicence nearest neighbor method nationallicence cross-validation nationallicence Paik Minhui Iowa State University aut Yang Yuhong Iowa State University aut Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-06-09), 1-19 3:1<1 2004 3 sagmb https://doi.org/10.2202/1544-6115.1054 text/html Onlinezugriff via DOI 1 research article jats NATIONALLICENCE 856 40 https://doi.org/10.2202/1544-6115.1054 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Paik Minhui Iowa State University aut NATIONALLICENCE 700 1- Yang Yuhong Iowa State University aut NATIONALLICENCE 773 0- Statistical Applications in Genetics and Molecular Biology De Gruyter 3/1(2004-06-09), 1-19 3:1<1 2004 3 sagmb CC0 http://creativecommons.org/publicdomain/zero/1.0 nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-gruyter