Extended Genetic Programming Using Apriori Algorithm for Rule Discovery

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ayahiko Niimi and Eiichiro Tazaki",
  title =        "Extended Genetic Programming Using Apriori Algorithm
                 for Rule Discovery",
  booktitle =    "New Frontiers in Artificial Intelligence : Joint JSAI
                 2001 Workshop Post-Proceedings",
  year =         "2001",
  editor =       "T. Terano and T. Nishida and A. Namatame and 
                 S. Tsumoto and Y. Ohsawa and T. Washio",
  volume =       "2253",
  series =       "Lecture Notes in Computer Science",
  pages =        "525--532",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-540-43070-4",
  CODEN =        "LNCSD9",
  ISSN =         "0302-9743",
  bibdate =      "Sat Feb 2 13:07:38 MST 2002",
  DOI =          "doi:10.1007/3-540-45548-5_73",
  acknowledgement = ack-nhfb,
  abstract =     "Genetic programming (GP) usually has a wide search
                 space and can use tree structure as its chromosome
                 expression. So, GP may search for global optimum
                 solution. But, in general, GP's learning speed is not
                 so fast. Apriori algorithm is one of algorithms for
                 generation of association rules. It can be applied to
                 large database. But, It is difficult to define its
                 parameters without experience. We propose a rule
                 discovery technique from a database using GP combined
                 with association rule algorithm. It takes rules
                 generated by the association rule algorithm as initial
                 individual of GP. The learning speed of GP is improved
                 by the combined algorithm. To verify the effectiveness
                 of the proposed method, we apply it to the
                 meningoencephalitis diagnosis activity data in a
                 hospital. We got domain expert's comments on our
                 results. We discuss the result of proposed method with
                 prior ones.",

Genetic Programming entries for Ayahiko Niimi Eiichiro Tazaki