A Cost-sensitive Genetic Programming Approach for High-dimensional Unbalanced Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Pei:2019:SSCI,
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author = "Wenbin Pei and Bing Xue and Mengjie Zhang and
Lin Shang",
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title = "A Cost-sensitive Genetic Programming Approach for
High-dimensional Unbalanced Classification",
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booktitle = "2019 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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year = "2019",
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pages = "1770--1777",
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abstract = "Many real-world classification problems encounter the
problem of class imbalance. Genetic programming (GP) is
one of the most important evolutionary algorithms,
making crucial contributions to classification,
particularly in the high-dimensional case. However,
like other classification algorithms, GP may also
develop biased classifiers when the class distribution
is unbalanced or skewed. This is because standard GP
treats each instance equally and assumes the same cost
of different misclassification. In unbalanced
classification, misclassification cost of the minority
class is often serious than that of the majority class.
Cost-sensitive learning has been successfully applied
to solve the problem of class imbalance for many
classification algorithms, but it has not been heavily
investigated in GP. This paper investigates how
cost-sensitive learning can be effectively used by GP
to address the problem of class imbalance in
high-dimensional unbalanced classification.
Experimental results on six high-dimensional unbalanced
datasets show the better performance of the proposed
methods than the compared methods.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/SSCI44817.2019.9003041",
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month = dec,
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notes = "Also known as \cite{9003041}",
- }
Genetic Programming entries for
Wenbin Pei
Bing Xue
Mengjie Zhang
Lin Shang
Citations