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The peaking phenomenon revisited: The case with feature selection

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2 Author(s)
Chao Sima ; Comput. Biol. Div., Translational Genomics Res. Inst., Phoenix, AZ ; Dougherty, E.R.

For a fixed sample size, a common phenomenon is that the error of a designed classifier decreases and then increases as the number of features grows. Historically this peaking phenomenon has been studied without taking into account feature selection, which is commonplace in high-dimensional settings. This paper revisits the peaking phenomenon in the presence of feature selection. The error curves tend to fall into three categories: peaking, settling into a plateau, or falling very slowly over a long range of feature-set sizes. It can be concluded that one should be wary of applying peaking results found in the absence of feature selection to settings in which feature selection is employed.

Published in:

Genomic Signal Processing and Statistics, 2008. GENSiPS 2008. IEEE International Workshop on

Date of Conference:

8-10 June 2008

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