Genetic programming for development of cost-sensitive classifiers for binary high-dimensional unbalanced classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{PEI:2021:ASC,
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author = "Wenbin Pei and Bing Xue and Lin Shang and
Mengjie Zhang",
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title = "Genetic programming for development of cost-sensitive
classifiers for binary high-dimensional unbalanced
classification",
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journal = "Applied Soft Computing",
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volume = "101",
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pages = "106989",
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year = "2021",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2020.106989",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494620309285",
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keywords = "genetic algorithms, genetic programming,
Classification, High-dimensionality, Class imbalance",
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abstract = "Genetic programming (GP) has the built-in ability for
feature selection when developing classifiers for
classification with high-dimensional data. However, due
to the problem of class imbalance, the developed
classifiers by GP are prone to be biased towards the
majority class. Cost-sensitive learning has shown to be
effective in addressing the problem of class imbalance.
In cost-sensitive learning, cost matrices are often
manually designed and then considered by classification
algorithms to treat different mistakes differently.
However, in many real-world applications, cost matrices
are unknown because of the limited domain knowledge in
complex situations. Therefore, in this paper, we
propose a novel GP method to develop cost-sensitive
classifiers, where a cost matrix is automatically
learned, instead of requiring it from domain experts.
The proposed method is examined and compared with
existing methods on ten high-dimensional unbalanced
datasets. Experimental results show that the proposed
method outperforms the compared GP methods in most
cases",
- }
Genetic Programming entries for
Wenbin Pei
Bing Xue
Lin Shang
Mengjie Zhang
Citations