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Title Genetic algorithm based neural classifiers for factor subset extraction
Edition Volume 12, Number 7
Call Number
ISBN/ISSN 1432-7643
Author(s) Lixu Qin
Simon X. Yang
Frank Pollari
Kathryn Dore
Aamir Fazil
Rafiq Ahmed
Jane Buxton
Karen Grimsrud
Series Title Soft Computing - A Fusion of Foundations, Methodologies and Applications
GMD Electronic Journal
Language English
Publisher Springer Berlin / Heidelberg
Publishing Year 2008
Publishing Place Berlin / Heidelberg
Collation 10p
Abstract/Notes A clear understanding of risk factors is important
to develop appropriate prevention and control strategies
for infection caused by such pathogens as Salmonella Typhimurium.
In this study, there are 91 risk factors that nonlinearly
contribute to the Salmonella Typhimurium infection
and many of them are not of significance. It is very important
to automatically extract a factor subset with those important
risk factors. This paper proposes a genetic algorithm for factor
subset extraction in conjunctionwith neural and statistical
classifiers to classify case and control status in Salmonella
Typhimurium infection. The results show that the proposed
approach is able to find an appropriate factor subset and the
proposed neural classifiers outperform the traditional statistical
classifiers. A statistical analysis is conducted by varying
the parameters in the genetic algorithm based neural classifier
L. Qin · S. X. Yang (B)
School of Engineering, University of Guelph,
Guelph, ON N1G 2W1, Canada
e-mail: syang@uoguelph.ca
F. Pollari · K. Dore
Foodborne, Waterborne and Zoonotic Infections Division,
Public Health Agency of Canada, Guelph, ON, Canada
A. Fazil
Laboratory for Foodborne Zoonoses,
Public Health Agency of Canada, Guelph, ON, Canada
R. Ahmed
National Microbiology Laboratory,
Public Health Agency of Canada, Winnipeg, MB, Canada
J. Buxton
British Columbia Centre for Disease Control,
Vancouver, BC, Canada
K. Grimsrud
Alberta Health and Wellness, Edmonton, AB, Canada
to minimise the prediction error and determine the optimal
system configuration.
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