An evolutionary approach to feature function generation in application to biomedical image patterns

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{DBLP:conf/gecco/GuoB09,
  author =       "Pei Fang Guo and Prabir Bhattacharya",
  title =        "An evolutionary approach to feature function
                 generation in application to biomedical image
                 patterns",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "1883--1884",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming, Poster",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  DOI =          "doi:10.1145/1569901.1570216",
  abstract =     "A mechanism involving evolutionary genetic programming
                 (GP) and the expectation maximization algorithm (EM) is
                 proposed to generate feature functions, based on the
                 primitive features, for an image pattern recognition
                 system on the diagnosis of the disease OPMD.
                 Experiments show that the propose algorithm achieves an
                 average performance of 90.20percent recognition rate on
                 diagnosis, while reducing the number of feature
                 dimensions from 11 primitive features to the space of a
                 single generated feature.",
  notes =        "Oculopharyngeal Muscular Dystrophy, CellDB grayscale
                 images, histogram region of interest by thresholds
                 (HROIT).

                 GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Pei Fang Guo Prabir Bhattacharya

Citations