Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
417298 | Computational Statistics & Data Analysis | 2008 | 13 Pages |
Abstract
The bias of the empirical error rate in supervised classification is studied. It is shown that this bias can be understood as a covariance between the classification rule and the labeling of the training data. From this result, a new penalized criterion is proposed to perform model selection in classification. Applications of the resulting algorithm to simulated and real data are presented.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Jean-Jacques Daudin, Tristan Mary-Huard,