Grammar guided genetic programming for multiple instance learning: an experimental study

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

  author =       "Amelia Zafra and Sebastian Ventura",
  title =        "Grammar guided genetic programming for multiple
                 instance learning: an experimental study",
  booktitle =    "GECCO '10: Proceedings of the 12th annual conference
                 on Genetic and evolutionary computation",
  year =         "2010",
  editor =       "Juergen Branke and Martin Pelikan and Enrique Alba and 
                 Dirk V. Arnold and Josh Bongard and 
                 Anthony Brabazon and Juergen Branke and Martin V. Butz and 
                 Jeff Clune and Myra Cohen and Kalyanmoy Deb and 
                 Andries P Engelbrecht and Natalio Krasnogor and 
                 Julian F. Miller and Michael O'Neill and Kumara Sastry and 
                 Dirk Thierens and Jano {van Hemert} and Leonardo Vanneschi and 
                 Carsten Witt",
  isbn13 =       "978-1-4503-0072-8",
  pages =        "909--916",
  keywords =     "genetic algorithms, genetic programming,
                 grammar-guided genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830483.1830647",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper introduces a new Grammar-Guided Genetic
                 Programming algorithm for solving multi-instance
                 Learning problems. This algorithm, called G3P-MI, is
                 evaluated and compared with other Multi-Instance
                 classification techniques on different application
                 domains. Computational experiments show that the G3P-MI
                 often obtains consistently better results than other
                 algorithms in terms of accuracy, sensitivity and
                 specificity. Moreover, it adds comprehensibility and
                 clarity into the knowledge discovery process,
                 expressing the information in the form of IF-THEN
                 rules. Our results confirm that evolutionary algorithms
                 are appropriate for dealing with multi-instance
                 learning problems.",
  notes =        "Also known as \cite{1830647} GECCO-2010 A joint
                 meeting of the nineteenth international conference on
                 genetic algorithms (ICGA-2010) and the fifteenth annual
                 genetic programming conference (GP-2010)",

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura