Towards a genetic programming algorithm for automatically evolving rule induction algorithms

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

@InProceedings{pappa:2004:ecml,
  author =       "Gisele L. Pappa and Alex A. Freitas",
  title =        "Towards a genetic programming algorithm for
                 automatically evolving rule induction algorithms",
  month =        "20-24 " # sep,
  year =         "2004",
  pages =        "93--108",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 rule induction, classification",
  URL =          "http://www.ke.informatik.tu-darmstadt.de/events/ECML-PKDD-04-WS/Proceedings/pappa.pdf",
  URL =          "http://www.cs.kent.ac.uk/pubs/2004/2023",
  publication_type = "inproceedings",
  submission_id = "4571_1102851561",
  booktitle =    "ECML/PKDD 2004 Proceedings of the Workshop W8 on
                 Advances in Inductive Learning",
  editor =       "Johannes Furnkranz",
  address =      "Pisa, Italy",
  refereed =     "yes",
  size =         "16 pages",
  abstract =     "Rule induction is one of the techniques most used to
                 extract knowledge from data, since the representation
                 of knowledge as if/then rules is very intuitive and
                 easily understandable by problem-domain experts.
                 Existing rule induction algorithms have been manually
                 designed. In this era of increasing automation, Genetic
                 Programming (GP) represents a powerful tool for
                 automatically evolving computer programs. This work
                 proposes a genetic programming algorithm for
                 automatically evolving rule induction algorithms.
                 Hence, the evolved rule induction algorithm will, to a
                 large extent, be free from the human biases that are
                 implicitly incorporated in current manually-designed
                 algorithms (such as the typical use of a greedy search
                 method). This is a very ambitious, adventurous goal,
                 which, if successful, will pave the way for a new
                 generation of more robust, considerably less greedy
                 rule induction algorithms. In particular, an
                 automatically evolved rule induction algorithm can be
                 designed to cope with attribute interaction better than
                 current greedy rule induction algorithms, which will
                 tend to lead to an improved performance in complex data
                 sets.",
  notes =        "http://www.cs.kent.ac.uk/pubs/2004/2023/index.html

                 http://www.ke.informatik.tu-darmstadt.de/events/ECML-PKDD-04-WS/",
}

Genetic Programming entries for Gisele L Pappa Alex Alves Freitas

Citations