Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection

Verfasser / Beitragende:
[Minhui Paik, Yuhong Yang]
Ort, Verlag, Jahr:
2004
Enthalten in:
Statistical Applications in Genetics and Molecular Biology, 3/1(2004-06-09), 1-19
Format:
Artikel (online)
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024 7 0 |a 10.2202/1544-6115.1054  |2 doi 
035 |a (NATIONALLICENCE)gruyter-10.2202/1544-6115.1054 
245 0 0 |a Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection  |h [Elektronische Daten]  |c [Minhui Paik, Yuhong Yang] 
520 3 |a 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. 
540 |a ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston 
690 7 |a Multivariate Analysis  |2 nationallicence 
690 7 |a combining classifiers  |2 nationallicence 
690 7 |a nearest neighbor method  |2 nationallicence 
690 7 |a cross-validation  |2 nationallicence 
700 1 |a Paik  |D Minhui  |u Iowa State University  |4 aut 
700 1 |a Yang  |D Yuhong  |u Iowa State University  |4 aut 
773 0 |t Statistical Applications in Genetics and Molecular Biology  |d De Gruyter  |g 3/1(2004-06-09), 1-19  |q 3:1<1  |1 2004  |2 3  |o sagmb 
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950 |B NATIONALLICENCE  |P 700  |E 1-  |a Yang  |D Yuhong  |u Iowa State University  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t Statistical Applications in Genetics and Molecular Biology  |d De Gruyter  |g 3/1(2004-06-09), 1-19  |q 3:1<1  |1 2004  |2 3  |o sagmb 
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