- author = "Yang Zhang and Peter I. Rockett",
- title = "Feature extraction using multi-objective genetic programming",
- booktitle = "Multi-Objective Machine Learning",
- publisher = "Springer",
- year = "2006",
- editor = "Yaochu Jin",
- volume = "16",
- series = "Studies in Computational Intelligence",
- chapter = "4",
- pages = "75--99",
- note = "Invited chapter",
- keywords = "genetic algorithms, genetic programming, Evolutionary Multi-objective Optimisation, Multi-objective Machine Learning",
- ISBN = "3-540-30676-5",
-
DOI = "
doi:10.1007/3-540-33019-4_4",
- abstract = "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 minimising 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.",
-
notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,4-175-22-106797000-detailsPage%253Dppmmedia%257CaboutThisBook%257CaboutThisBook,00.html
",
