Multiclass Classification on High Dimension and Low Sample Size Data using Genetic Programming
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- @Article{Tingyang_Wei:ETC,
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author = "Tingyang Wei and Wei-Li Liu and Jinghui Zhong and
Yue-Jiao Gong",
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title = "Multiclass Classification on High Dimension and Low
Sample Size Data using Genetic Programming",
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journal = "IEEE Transactions on Emerging Topics in Computing",
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year = "2022",
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volume = "10",
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number = "2",
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pages = "704--718",
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming",
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ISSN = "2168-6750",
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DOI = "doi:10.1109/TETC.2020.3034495",
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abstract = "Multiclass classification is one of the most
fundamental tasks in data mining. However, traditional
data mining methods rely on the model assumption, they
generally can suffer from the overfitting problem on
high dimension and low sample size (HDLSS) data. Trying
to address multiclass classification problems on HDLSS
data from another perspective, we use Genetic
Programming (GP), an intrinsic evolutionary
classification algorithm that can implement feature
construction automatically without model assumption.
This paper develops an ensemble-based genetic
programming classification framework, the Sigmoid-based
Ensemble Gene Expression Programming (SEGEP). To
relieve the problem of output conflict in GP-based
multiclass classifiers, the proposed method employs a
flexible probability representation with continuous
relaxation to better integrate the output of all the
binary classifiers, an effective data division strategy
to further enhance the ensemble performance, and a
novel sampling strategy to refine the existing GP-based
binary classifier. The experiment results indicate that
SE-GEP can attain better classification accuracy
compared to other GP methods. Moreover, the comparison
with other representative machine learning methods
indicates that SE-GEP is a competitive method for
multiclass classification in HDLSS data.",
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notes = "Also known as \cite{9242277}",
- }
Genetic Programming entries for
Tingyang Wei
Weili Liu
Jinghui Zhong
Yue-Jiao Gong
Citations