Genetic Programming for Classification with Unbalanced Data
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gp-bibliography.bib Revision:1.7954
- @InProceedings{Bhowan:2010:EuroGP,
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author = "Urvesh Bhowan and Mengjie Zhang and Mark Johnston",
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title = "Genetic Programming for Classification with Unbalanced
Data",
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booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
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year = "2010",
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editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and
Sara Silva and Stephen Dignum and A. Sima Uyar",
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volume = "6021",
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series = "LNCS",
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pages = "1--13",
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address = "Istanbul",
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month = "7-9 " # apr,
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organisation = "EvoStar",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-12147-0",
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DOI = "doi:10.1007/978-3-642-12148-7_1",
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abstract = "Learning algorithms can suffer a performance bias when
data sets only have a small number of training examples
for one or more classes. In this scenario learning
methods can produce the deceptive appearance of good
looking results even when classification performance on
the important minority class can be poor. This paper
compares two Genetic Programming (GP) approaches for
classification with unbalanced data. The first focuses
on adapting the fitness function to evolve classifiers
with good classification ability across both minority
and majority classes. The second uses a multi-objective
approach to simultaneously evolve a Pareto front (or
set) of classifiers along the minority and majority
class trade-off surface. Our results show that
solutions with good classification ability were evolved
across a range of binary classification tasks with
unbalanced data.",
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notes = "Part of \cite{Esparcia-Alcazar:2010:GP} EuroGP'2010
held in conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
- }
Genetic Programming entries for
Urvesh Bhowan
Mengjie Zhang
Mark Johnston
Citations