Drawing boundaries: using individual evolved class boundaries for binary classification problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Jeannie Fitzgerald and Conor Ryan",
  title =        "Drawing boundaries: using individual evolved class
                 boundaries for binary classification problems",
  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 =        "1347--1354",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001576.2001758",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper describes a technique which can be used
                 with Genetic Programming (GP) to reduce implicit bias
                 in binary classification tasks. Arbitrarily chosen
                 class boundaries can introduce bias, but if individuals
                 can choose their own boundaries, tailored to their
                 function set, then their outputs are automatically
                 scaled into a suitable range. These boundaries evolve
                 over time as the individuals adapt to the data. Our
                 system calculates the Evolved Class Boundary(ECB) for
                 each individual in every generation, with the twin aims
                 of reducing training times and improving test fitness.
                 The method is tested on three benchmark binary
                 classification data sets from the medical domain.

                 The results obtained suggest that the strategy can
                 improve training, validation and test fitness, and can
                 also result in smaller individuals as well as reduced
                 training times. Our approach is compared with a
                 standard benchmark GP system, as well as with over
                 twenty other systems from the literature, many of which
                 use highly tuned, non-EC methods, and is shown to yield
                 superior results in many cases.",
  notes =        "Also known as \cite{2001758} 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 Jeannie Fitzgerald Conor Ryan