Using Genetic Programming to Increase Rule Quality

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

@InProceedings{DBLP:conf/flairs/KonigJN08,
  author =       "Rikard Konig and Ulf Johansson and Lars Niklasson",
  title =        "Using Genetic Programming to Increase Rule Quality",
  booktitle =    "Proceedings of the Twenty-First International Florida
                 Artificial Intelligence Research Society Conference",
  year =         "2008",
  editor =       "David Wilson and H. Chad Lane",
  publisher =    "AAAI Press",
  pages =        "288--293",
  address =      "Coconut Grove, Florida, USA",
  month =        may # " 15-17",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-57735-365-2",
  URL =          "http://www.aaai.org/Papers/FLAIRS/2008/FLAIRS08-071.pdf",
  size =         "6 pages",
  abstract =     "Rule extraction is a technique aimed at transforming
                 highly accurate opaque models like neural networks into
                 comprehensible models without losing accuracy. G-REX is
                 a rule extraction technique based on Genetic
                 Programming that previously has performed well in
                 several studies. This study has two objectives, to
                 evaluate two new fitness functions for G-REX and to
                 show how G-REX can be used as a rule inducer.

                 The fitness functions are designed to optimize two
                 alternative quality measures, area under ROC curves and
                 a new comprehensibility measure called brevity. Rules
                 with good brevity classifies typical instances with few
                 and simple tests and use complex conditions only for
                 atypical examples. Experiments using thirteen publicly
                 available data sets show that the two novel fitness
                 functions succeeded in increasing brevity and area
                 under the ROC curve without sacrificing accuracy. When
                 compared to a standard decision tree algorithm, G-REX
                 achieved slightly higher accuracy, but also added
                 additional quality to the rules by increasing their AUC
                 or brevity significantly.",
  notes =        "BNF grammar Fig 2.",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
}

Genetic Programming entries for Rikard Konig Ulf Johansson Lars Niklasson

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