Combining Evolutionary, Connectionist, and Fuzzy Classification Algorithms for Shape Amalysis

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{rosin:2000:cecfcasa,
  author =       "Paul L. Rosin and Henry O. Nyongesa",
  title =        "Combining Evolutionary, Connectionist, and Fuzzy
                 Classification Algorithms for Shape Amalysis",
  booktitle =    "Real-World Applications of Evolutionary Computing",
  year =         "2000",
  editor =       "Stefano Cagnoni and Riccardo Poli and 
                 George D. Smith and David Corne and Martin Oates and Emma Hart and 
                 Pier Luca Lanzi and Egbert Jan Willem and Yun Li and 
                 Ben Paechter and Terence C. Fogarty",
  volume =       "1803",
  series =       "LNCS",
  pages =        "87--96",
  address =      "Edinburgh",
  publisher_address = "Berlin",
  month =        "17 " # apr,
  organisation = "EvoNet",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, C4.5, OC1,
                 Fuzzy IF-THEN rules, ANN, lilgp",
  ISBN =         "3-540-67353-9",
  broken =       "http://www.cs.cf.ac.uk/resources/papers/beans.ps.gz",
  URL =          "http://citeseer.ist.psu.edu/rosin00combining.html",
  URL =          "http://www.springerlink.com/openurl.asp?genre=article&issn=0302-9743&volume=1803&spage=87",
  abstract =     "the classification of a difficult data set containing
                 large intra-class variability but low inter-class
                 variability. Standard classifiers are weak and fail to
                 achieve satisfactory results however, it is proposed
                 that a combination of such weak classifiers can improve
                 overall performance. The paper also introduces a novel
                 evolutionary approach to fuzzy rule generation for
                 classification problems.",
  notes =        "{"}...fuzzy classification rules of arbitrary size and
                 structure can be generated using genetic programming{"}
                 page90. Voting Schemes, confusion matrix. Seed shapes:
                 130 examples each with 17 continuous attributes from 9
                 species. {"}...no significant differences between the
                 individual techniques on our classification problem.
                 However, we have shown improvements can be achieved
                 through different combinations of these
                 techniques.

                 EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM,
                 EvoRob, and EvoFlight, Edinburgh, Scotland, UK, April
                 17, 2000
                 Proceedings

                 http://evonet.lri.fr/evoweb/resources/books_journals/record.php?id=61",
}

Genetic Programming entries for Paul L Rosin Henry Nyongesa

Citations