Anzeige
Erschienen in:
2006 | OriginalPaper | Buchkapitel
Feature Extraction Using Multi-Objective Genetic Programming
verfasst von : Yang Zhang, Peter I Rockett
Erschienen in: Multi-Objective Machine Learning
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
A generic, optimal feature extraction method using multi-objective genetic programming (MOGP) is presented. This methodology has been applied to the well-known edge detection problem in image processing and detailed comparisons made with the Canny edge detector. We show that the superior performance from MOGP in terms of minimizing the misclassification is due to its effective
optimal
feature extraction. Furthermore, to compare different evolutionary approaches, two popular techniques - PCGA and SPGA - have been extended to genetic programming as PCGP and SPGP, and applied to five datasets from the UCI database. Both of these evolutionary approaches provide comparable misclassification errors within the present framework but PCGP produces more compact transformations.