Genetically Evolved Trees Representing Ensembles

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

@InProceedings{Johansson:2006:ICAISC,
  author =       "Ulf Johansson and Tuve Lofstrom and Rikard Konig and 
                 Lars Niklasson",
  title =        "Genetically Evolved Trees Representing Ensembles",
  booktitle =    "Proceedings 8th International Conference on Artificial
                 Intelligence and Soft Computing {ICAISC}",
  year =         "2006",
  pages =        "613--622",
  series =       "Lecture Notes on Artificial Intelligence (LNAI)",
  volume =       "4029",
  publisher =    "Springer-Verlag",
  editor =       "Leszek Rutkowski and Ryszard Tadeusiewicz and 
                 Lotfi A. Zadeh and Jacek Zurada",
  address =      "Zakopane, Poland",
  month =        jun # " 25-29",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-35748-3",
  DOI =          "doi:10.1007/11785231_64",
  size =         "10 pages",
  abstract =     "We have recently proposed a novel algorithm for
                 ensemble creation called GEMS (Genetic Ensemble Member
                 Selection). GEMS first trains a fixed number of neural
                 networks (here twenty) and then uses genetic
                 programming to combine these networks into an ensemble.
                 The use of genetic programming makes it possible for
                 GEMS to not only consider ensembles of different sizes,
                 but also to use ensembles as intermediate building
                 blocks. In this paper, which is the first extensive
                 study of GEMS, the representation language is extended
                 to include tests partitioning the data, further
                 increasing flexibility. In addition, several micro
                 techniques are applied to reduce overfitting, which
                 appears to be the main problem for this powerful
                 algorithm. The experiments show that GEMS, when
                 evaluated on 15 publicly available data sets, obtains
                 very high accuracy, clearly outperforming both
                 straightforward ensemble designs and standard decision
                 tree algorithms.",
}

Genetic Programming entries for Ulf Johansson Tuve Lofstrom Rikard Konig Lars Niklasson

Citations