Exploring boundaries: optimising individual class boundaries for binary classification problem

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Jeannie Fitzgerald and Conor Ryan",
  title =        "Exploring boundaries: optimising individual class
                 boundaries for binary classification problem",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  editor =       "Terry Soule and Anne Auger and Jason Moore and 
                 David Pelta and Christine Solnon and Mike Preuss and 
                 Alan Dorin and Yew-Soon Ong and Christian Blum and 
                 Dario Landa Silva and Frank Neumann and Tina Yu and 
                 Aniko Ekart and Will Browne and Tim Kovacs and 
                 Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and 
                 Giovanni Squillero and Nicolas Bredeche and 
                 Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and 
                 Martin Pelikan and Silja Meyer-Nienberg and 
                 Christian Igel and Greg Hornby and Rene Doursat and 
                 Steve Gustafson and Gustavo Olague and Shin Yoo and 
                 John Clark and Gabriela Ochoa and Gisele Pappa and 
                 Fernando Lobo and Daniel Tauritz and Jurgen Branke and 
                 Kalyanmoy Deb",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "743--750",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330163.2330267",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "This paper explores a range of class boundary
                 determination techniques that can be used to improve
                 performance of Genetic Programming (GP) on binary
                 classification tasks. These techniques involve
                 selecting an individualised boundary threshold in order
                 to reduce implicit bias that may be introduced through
                 employing arbitrarily chosen values. Individuals that
                 can chose their own boundaries and the manner in which
                 they are applied, are freed from having to learn to
                 force their outputs into a particular range or polarity
                 and can instead concentrate their efforts on seeking a
                 problem solution.

                 Our investigation suggests that while a particular
                 boundary selection method may deliver better
                 performance for a given problem, no single method
                 performs best on all problems studied. We propose a new
                 flexible combined technique which gives near optimal
                 performance across each of the tasks undertaken. This
                 method together with seven other techniques is tested
                 on six benchmark binary classification data sets.
                 Experimental results obtained suggest that the strategy
                 can improve test fitness, produce smaller less complex
                 individuals and reduce run times. Our approach is shown
                 to deliver superior results when benchmarked against a
                 standard GP system, and is very competitive when
                 compared with a range of other machine learning
  notes =        "Also known as \cite{2330267} GECCO-2012 A joint
                 meeting of the twenty first international conference on
                 genetic algorithms (ICGA-2012) and the seventeenth
                 annual genetic programming conference (GP-2012)",

Genetic Programming entries for Jeannie Fitzgerald Conor Ryan