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Feature selection increases cross-validation imprecision

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3 Author(s)
Yufei Xiao ; Dept. of Electr. Eng., Texas A&M Univ., College Station, TX ; Jianping Hua ; Dougherty, E.R.

Even without feature selection, cross-validation error estimation is problematic for small samples owing to the high variance of the deviation distribution describing the difference between the estimated and true errors. This paper investigates the increased loss of cross-validation precision owing to feature selection by comparing deviation distributions and introducing two variation-based measures to quantify the further degradation in performance.

Published in:

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Date of Conference:

28-30 May 2006