The Truth is In There - Rule Extraction from Opaque Models Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Ulf Johansson and Rikard Konig and Lars Niklasson",
  title =        "The Truth is In There - Rule Extraction from Opaque
                 Models Using Genetic Programming",
  booktitle =    "Proceedings of the Seventeenth International Florida
                 Artificial Intelligence Research Society Conference",
  year =         "2004",
  editor =       "Valerie Barr and Zdravko Markov",
  address =      "Miami Beach, Florida, USA",
  publisher =    "AAAI Press",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-57735-201-7",
  URL =          "",
  size =         "6 pages",
  abstract =     "A common problem when using complicated models for
                 prediction and classification is that the complexity of
                 the model entails that it is hard, or impossible, to
                 interpret. For some scenarios this might not be a
                 limitation, since the priority is the accuracy of the
                 model. In other situations the limitations might be
                 severe, since additional aspects are important to
                 consider; e.g. comprehensibility or scalability of the
                 model. In this study we show how the gap between
                 accuracy and other aspects can be bridged by using a
                 rule extraction method (termed G-REX) based on genetic
                 programming. The extraction method is evaluated against
                 the five criteria accuracy, comprehensibility,
                 fidelity, scalability and generality. It is also shown
                 how G-REX can create novel representation languages;
                 here regression trees and fuzzy rules. The problem used
                 is a data-mining problem from the marketing domain
                 where the impact of advertising is predicted from
                 investment plans. Several experiments, covering both
                 regression and classification tasks, are evaluated.
                 Results show that G-REX in general is capable of
                 extracting both accurate and comprehensible
                 representations, thus allowing high performance also in
                 domains where comprehensibility is of essence.",
  bibsource =    "DBLP,",

Genetic Programming entries for Ulf Johansson Rikard Konig Lars Niklasson