By Topic

A Note on Generalization Loss When Evolving Adaptive Pattern Recognition Systems

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
$31 $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

1 Author(s)
Igel, C. ; Dept. of Comput. Sci., Univ. of Copenhagen, Copenhagen, Denmark

Evolutionary computing provides powerful methods for designing pattern recognition systems. This design process is typically based on finite sample data and therefore bears the risk of overfitting. This paper aims at raising the awareness of various types of overfitting and at providing guidelines for how to deal with them. We restrict our considerations to the predominant scenario in which fitness computations are based on point estimates. Three different sources of losing generalization performance when evolving learning machines, namely overfitting to training, test, and final selection data, are identified, discussed, and experimentally demonstrated. The importance of a pristine hold-out data set for the selection of the final result from the evolved candidates is highlighted. It is shown that it may be beneficial to restrict this last selection process to a subset of the evolved candidates.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:17 ,  Issue: 3 )