- 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)",
