Abstract
Data preprocessing, especially in terms of feature selection and generation, is an important issue in data mining and knowledge discovery tasks. Genetic algorithms proved to work well on feature selection problems where the search space produced by the initial feature set already contains the target hypothesis. In cases where this precondition is not fulfilled, one needs to construct new features to adequately extend the search space. As a solution to this representation problem, we introduce a framework combining feature selection and type-restricted feature generation in a wrapper-based approach using a modified canonical genetic algorithm for the feature space transformation and an inductive learner for the evaluation of the constructed feature set.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
S. Fischer, R. Klinkenberg, I. Mierswa, and O. Ritthoff. Tutorial for yale: Yet Another Learning Environment. Technical Report CI 136/02, SFB 531, University of Dortmund, June 2002. http://yale.uni-dortmund.de/.
H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, Dordrecht, NL, 1998.
M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, 1996.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ritthoff, O., Klinkenberg, R. (2003). Evolutionary Feature Space Transformation Using Type-Restricted Generators. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_47
Download citation
DOI: https://doi.org/10.1007/3-540-45110-2_47
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40603-7
Online ISBN: 978-3-540-45110-5
eBook Packages: Springer Book Archive