- 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 conference",
- 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 algorithms.",
- 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)",
