Sampling Methods in Genetic Programming for Classification with Unbalanced Data
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Hunt:2010:ACAI,
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author = "Rachel Hunt and Mark Johnston and Will N. Browne and
Mengjie Zhang",
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title = "Sampling Methods in Genetic Programming for
Classification with Unbalanced Data",
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booktitle = "Australasian Conference on Artificial Intelligence",
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year = "2010",
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editor = "Jiuyong Li",
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volume = "6464",
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series = "Lecture Notes in Computer Science",
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pages = "273--282",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-17431-5",
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DOI = "doi:10.1007/978-3-642-17432-2_28",
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size = "10 pages",
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bibdate = "2010-11-30",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ausai/ausai2010.html#HuntJBZ10",
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abstract = "This work investigates the use of sampling methods in
Genetic Programming (GP) to improve the classification
accuracy in binary classification problems in which the
datasets have a class imbalance. Class imbalance occurs
when there are more data instances in one class than
the other. As a consequence of this imbalance, when
overall classification rate is used as the fitness
function, as in standard GP approaches, the result is
often biased towards the majority class, at the expense
of poor minority class accuracy. We establish that the
variation in training performance introduced by
sampling examples from the training set is no worse
than the variation between GP runs already accepted.
Results also show that the use of sampling methods
during training can improve minority class
classification accuracy and the robustness of
classifiers evolved, giving performance on the test set
better than that of those classifiers which made up the
training set Pareto front.",
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affiliation = "School of Mathematics, Statistics and Operations
Research, Victoria University of Wellington, P.O. Box
600, Wellington, New Zealand",
- }
Genetic Programming entries for
Rachel Hunt
Mark Johnston
Will N Browne
Mengjie Zhang
Citations