Genetic Programming - A Tool for Flexible Rule Extraction

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

@InProceedings{Konig:2007:cec,
  author =       "R. Konig and U. Johansson and L. Niklasson",
  title =        "Genetic Programming - A Tool for Flexible Rule
                 Extraction",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "1304--1310",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1989.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424621",
  abstract =     "Although data mining is performed to support decision
                 making, many of the most powerful techniques, like
                 neural networks and ensembles, produce opaque models.
                 This lack of interpretability is an obvious
                 disadvantage, since decision makers normally require
                 some sort of explanation before taking action. To
                 achieve comprehensibility, accuracy is often sacrificed
                 by the use of simpler, transparent models, such as
                 decision trees. Another alternative is rule extraction;
                 i.e. to transform the opaque model into a
                 comprehensible model, keeping acceptable accuracy. We
                 have previously suggested a rule extraction algorithm
                 named G-REX, which is based on genetic programming. One
                 key property of G-REX, due to the use of genetic
                 programming, is the possibility to use different
                 representation languages. In this study we apply G-REX
                 to estimation tasks. More specifically, three
                 representation languages are evaluated using eight
                 publicly available data sets. The quality of the
                 extracted rules is compared to two standard techniques
                 producing comprehensible models; multiple linear
                 regression and the decision tree algorithm C&RT.
                 The results show that G-REX outperforms the standard
                 techniques, but that the choice of representation
                 language is important.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",
}

Genetic Programming entries for Rikard Konig Ulf Johansson Lars Niklasson

Citations