Design of Decision Trees through Integration of C4.5 and GP

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Oka:2000:DDTICGP,
  author =       "Shin'ichi Oka and Qiangfu Zhao",
  title =        "Design of Decision Trees through Integration of {C4.5}
                 and GP",
  booktitle =    "Proceedings of the fourth Japan-Australia Joint
                 Workshop on Intelligent and Evolutionary Systems",
  year =         "2000",
  editor =       "Akira Namatame {et al.}",
  pages =        "128--135",
  address =      "Shonan Village Center, Hayama, Japan",
  month =        "31 " # oct # " - 2 " # nov,
  publisher =    "Japan. National Defence Academy ; Australia.
                 Australian Defence Force Academy",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7317-0504-1",
  URL =          "http://www.u-aizu.ac.jp/~qf-zhao/CONTRIBUTION/oka-zhao.ps.Z",
  URL =          "http://130.203.133.150/viewdoc/summary?doi=10.1.1.23.3179",
  catalogue-url = "http://nla.gov.au/nla.cat-vn1175103",
  size =         "6 pages",
  abstract =     "C4.5 is one of the tools for designing decision trees
                 (DTs) from training examples. In most cases, C4.5 can
                 generate near optimal DTs when the training data are
                 given all together. However, if the training data are
                 given incrementally, C4.5 cannot be used. In this case,
                 genetic programming (GP) might be a better choice.
                 Actually, GP can be considered as a DT-breeder in which
                 good DTs can be generated automatically through
                 evolution. In GP based DT design, the training examples
                 can be given all together or incrementally, provided
                 that the fitness of the tree is properly defined. This
                 of course does NOT mean that the GP based approach is
                 BETTER than C4.5 because DTs obtained by GP are usually
                 very large and complex. In this paper, we try to
                 integrate C4.5 and GP in such a way that each
                 individual is initialised by C4.5 using part of the
                 training examples. By so doing, we can have relatively
                 good DTs from the very beginning, and use them while
                 waiting for better DTs to emerge. To show the
                 effectiveness of this kind of integration, we conducted
                 some experiments with a digit recognition problem.
                 Experimental results show that smaller DTs with higher
                 recognition rates can always be obtained through
                 integration of C4.5 and GP. However, as the evolution
                 continues, DTs obtained by GP (with random
                 initialisation) tend to have almost the same
                 recognition ability as those obtained by C4.5+GP.",
  notes =        "Selected Refereed Publications of Qiangfu Zhao and His
                 Students

                 National library of Australia
                 http://catalogue.nla.gov.au/Record/1175103

                 http://www.uco.es/grupos/kdis/kdiswiki/gp/GP_bibliography_bib.html#Oka:2000:DDTICGP",
}

Genetic Programming entries for Shin'ichi Oka Qiangfu Zhao

Citations