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Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection

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  • Paik Minhui

    (Iowa State University)

  • Yang Yuhong

    (Iowa State University)

Abstract

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.

Suggested Citation

  • Paik Minhui & Yang Yuhong, 2004. "Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-21, June.
    • Handle: RePEc:bpj:sagmbi:v:3:y:2004:i:1:n:12
    DOI: 10.2202/1544-6115.1054
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    Cited by:

    1. Ghosh, Anil K., 2006. "On optimum choice of k in nearest neighbor classification," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3113-3123, July.

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