Building Neural Network Ensembles using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Ulf Johansson and Tuve Lofstrom and Rikard Konig and 
                 Lars Niklasson",
  title =        "Building Neural Network Ensembles using Genetic
  booktitle =    "Proceedings of the 2006 IEEE Congress on Evolutionary
  year =         "2006",
  editor =       "Gary G. Yen and Lipo Wang and Piero Bonissone and 
                 Simon M. Lucas",
  pages =        "2239--2244",
  address =      "Vancouver",
  month =        "6-21 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7803-9487-9",
  DOI =          "doi:10.1109/IJCNN.2006.246836",
  size =         "6 pages",
  abstract =     "In this paper we present and evaluate a novel
                 algorithm for ensemble creation. The main idea of the
                 algorithm is to first independently train a fixed
                 number of neural networks (here ten) and then use
                 genetic programming to combine these networks into an
                 ensemble. The use of genetic programming makes it
                 possible to not only consider ensembles of different
                 sizes, but also to use ensembles as intermediate
                 building blocks. The final result is therefore more
                 correctly described as an ensemble of neural network
                 ensembles. The experiments show that the proposed
                 method, when evaluated on 22 publicly available data
                 sets, obtains very high accuracy, clearly outperforming
                 the other methods evaluated. In this study several
                 micro techniques are used, and we believe that they all
                 contribute to the increased performance. One such micro
                 technique, aimed at reducing overtraining, is the
                 training method, called tombola training, used during
                 genetic evolution. When using tombola training,
                 training data is regularly resampled into new parts,
                 called training groups. Each ensemble is then evaluated
                 on every training group and the actual fitness is
                 determined solely from the result on the hardest
  notes =        "WCCI 2006 - A joint meeting of the IEEE, the EPS, and
                 the IEE.

                 IEEE Catalog Number: 06TH8846D",

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