By Topic

The peaking phenomenon revisited: The case with feature selection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chao Sima ; Computational Biology Division, Translational Genomics Research Institute, Phoenix, AZ 85004, USA ; Edward R. Dougherty

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:

2008 IEEE International Workshop on Genomic Signal Processing and Statistics

Date of Conference:

8-10 June 2008