A Threshold-free Classification Mechanism in Genetic Programming for High-dimensional Unbalanced Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Pei:2020:CEC,
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author = "Wenbin Pei and Bing Xue and Lin Shang and
Mengjie Zhang",
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title = "A Threshold-free Classification Mechanism in Genetic
Programming for High-dimensional Unbalanced
Classification",
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booktitle = "2020 IEEE Congress on Evolutionary Computation, CEC
2020",
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year = "2020",
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editor = "Yaochu Jin",
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pages = "paper id24341",
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address = "internet",
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month = "19-24 " # jul,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CEC48606.2020.9185503",
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abstract = "Class imbalance is an unavoidable issue in many
real-world applications. Learning from unbalanced data,
classifiers are often biased toward the majority class,
while the minority class is important as well (even
more important in many cases). How the issue of class
imbalance is addressed becomes more challenging if a
classification task further encounters the high
dimensionality issue. This paper proposes a new genetic
programming (GP) approach to high-dimensional
unbalanced classification. A new classification
mechanism is proposed for GP to improve its
classification performance. This new classification
mechanism is independent of a classification threshold
to separate the majority class and the minority class.
The effectiveness of the proposed method is examined on
seven high-dimensional unbalanced datasets.
Experimental results indicate that the proposed GP
method often performs better than other GP methods that
use a fitness function to solve the issue of class
imbalance, in terms of classification performance and
training time.",
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notes = "https://wcci2020.org/
Victoria University of Wellington, New Zealand; Nanjing
University, China.
Also known as \cite{9185503}",
- }
Genetic Programming entries for
Wenbin Pei
Bing Xue
Lin Shang
Mengjie Zhang
Citations