High-dimensional Unbalanced Binary Classification by Genetic Programming with Multi-criterion Fitness Evaluation and Selection
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Pei:2022:EC,
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author = "Wenbin Pei and Bing Xue and Lin Shang and
Mengjie Zhang",
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title = "High-dimensional Unbalanced Binary Classification by
Genetic Programming with Multi-criterion Fitness
Evaluation and Selection",
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journal = "Evolutionary Computation",
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year = "2022",
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volume = "30",
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number = "1",
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pages = "99--129",
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month = "Spring",
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keywords = "genetic algorithms, genetic programming,
Classification, High dimensionality, Class imbalance",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/evco_a_00304",
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size = "31 pages",
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abstract = "High-dimensional unbalanced classification is
challenging because of the joint effects of high
dimensionality and class imbalance. Genetic programming
(GP) has the potential benefits for use in
high-dimensional classification due to its built-in
capability to select informative features. However,
once data is not evenly distributed, GP tends to
develop biased classifiers which achieve a high
accuracy on the majority class but a low accuracy on
the minority class. Unfortunately, the minority class
is often at least as important as the majority class.
It is of importance to investigate how GP can be
effectively used for high-dimensional unbalanced
classification. to address the performance bias issue
of GP, a new two-criterion fitness function is
developed, which considers two criteria, i.e. the
approximation of area under the curve (AUC) and the
classification clarity (i.e. how well a program can
separate two classes). The obtained values on the two
criteria are combined in pairs, instead of summing them
together. Furthermore, this paper designs a
three-criterion tournament selection to effectively
identify and select good programs to be used by genetic
operators for generating better offspring during the
evolutionary learning process. The experimental results
show that the proposed method achieves better
classification performance than other compared
methods.",
- }
Genetic Programming entries for
Wenbin Pei
Bing Xue
Lin Shang
Mengjie Zhang
Citations