Evolving ensembles in multi-objective genetic programming for classification with unbalanced data

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

@InProceedings{Bhowan:2011:GECCO,
  author =       "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
  title =        "Evolving ensembles in multi-objective genetic
                 programming for classification with unbalanced data",
  booktitle =    "GECCO '11: Proceedings of the 13th annual conference
                 on Genetic and evolutionary computation",
  year =         "2011",
  editor =       "Natalio Krasnogor and Pier Luca Lanzi and 
                 Andries Engelbrecht and David Pelta and Carlos Gershenson and 
                 Giovanni Squillero and Alex Freitas and 
                 Marylyn Ritchie and Mike Preuss and Christian Gagne and 
                 Yew Soon Ong and Guenther Raidl and Marcus Gallager and 
                 Jose Lozano and Carlos Coello-Coello and Dario Landa Silva and 
                 Nikolaus Hansen and Silja Meyer-Nieberg and 
                 Jim Smith and Gus Eiben and Ester Bernado-Mansilla and 
                 Will Browne and Lee Spector and Tina Yu and Jeff Clune and 
                 Greg Hornby and Man-Leung Wong and Pierre Collet and 
                 Steve Gustafson and Jean-Paul Watson and 
                 Moshe Sipper and Simon Poulding and Gabriela Ochoa and 
                 Marc Schoenauer and Carsten Witt and Anne Auger",
  isbn13 =       "978-1-4503-0557-0",
  pages =        "1331--1338",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001756",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Machine learning algorithms can suffer a performance
                 bias when data sets are unbalanced. This paper proposes
                 a Multi-objective Genetic Programming approach using
                 negative correlation learning to evolve accurate and
                 diverse ensembles of non-dominated solutions where
                 members vote on class membership. We also compare two
                 popular Pareto-based fitness schemes on the
                 classification tasks. We show that the evolved
                 ensembles achieve high accuracy on both classes using
                 six unbalanced binary data sets, and that this
                 performance is usually better than many of its
                 individual members.",
  notes =        "Also known as \cite{2001756} GECCO-2011 A joint
                 meeting of the twentieth international conference on
                 genetic algorithms (ICGA-2011) and the sixteenth annual
                 genetic programming conference (GP-2011)",
}

Genetic Programming entries for Urvesh Bhowan Mark Johnston Mengjie Zhang

Citations