Comparing ensemble learning approaches in genetic programming for classification with unbalanced data

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Urvesh Bhowan and Mark Johnston and Mengjie Zhang",
  title =        "Comparing ensemble learning approaches in genetic
                 programming for classification with unbalanced data",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and 
                 Thomas Bartz-Beielstein and Daniele Loiacono and 
                 Francisco Luna and Joern Mehnen and Gabriela Ochoa and 
                 Mike Preuss and Emilia Tantar and Leonardo Vanneschi and 
                 Kent McClymont and Ed Keedwell and Emma Hart and 
                 Kevin Sim and Steven Gustafson and 
                 Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and 
                 Nikolaus Hansen and Olaf Mersmann and Petr Posik and 
                 Heike Trautmann and Muhammad Iqbal and Kamran Shafi and 
                 Ryan Urbanowicz and Stefan Wagner and 
                 Michael Affenzeller and David Walker and Richard Everson and 
                 Jonathan Fieldsend and Forrest Stonedahl and 
                 William Rand and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and Gisele L. Pappa and 
                 John Woodward and Jerry Swan and Krzysztof Krawiec and 
                 Alexandru-Adrian Tantar and Peter A. N. Bosman and 
                 Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and 
                 David L. Gonzalez-Alvarez and 
                 Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and 
                 Kenneth Holladay and Tea Tusar and Boris Naujoks",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "135--136",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2464643",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper compares three approaches to evolving
                 ensembles in Genetic Programming (GP) for binary
                 classification with unbalanced data. The first uses
                 bagging with sampling, while the other two use
                 Pareto-based multi-objective GP (MOGP) for the
                 trade-off between the two (unequal) classes. In MOGP,
                 two ways are compared to build the ensembles: using the
                 evolved Pareto front alone, and using the whole evolved
                 population of dominated and non-dominated individuals
                 alike. Experiments on several benchmark (binary)
                 unbalanced tasks find that smaller, more diverse
                 ensembles chosen during ensemble selection perform best
                 due to better generalisation, particularly when the
                 combined knowledge of the whole evolved MOGP population
                 forms the ensemble.",
  notes =        "Also known as \cite{2464643} Distributed at

Genetic Programming entries for Urvesh Bhowan Mark Johnston Mengjie Zhang