Feature extraction using multi-objective genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InCollection{Zhang:2006:MOML,
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author = "Yang Zhang and Peter I. Rockett",
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title = "Feature extraction using multi-objective genetic
programming",
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booktitle = "Multi-Objective Machine Learning",
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publisher = "Springer",
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year = "2006",
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editor = "Yaochu Jin",
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volume = "16",
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series = "Studies in Computational Intelligence",
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chapter = "4",
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pages = "75--99",
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note = "Invited chapter",
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keywords = "genetic algorithms, genetic programming, Evolutionary
Multi-objective Optimisation, Multi-objective Machine
Learning",
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ISBN = "3-540-30676-5",
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DOI = "doi:10.1007/3-540-33019-4_4",
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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.",
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notes = "http://www.springer.com/sgw/cda/frontpage/0,11855,4-175-22-106797000-detailsPage%253Dppmmedia%257CaboutThisBook%257CaboutThisBook,00.html
",
- }
Genetic Programming entries for
Yang Zhang
Peter I Rockett
Citations