Genetic Programming for Automatically Evolving Multiple Features to Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Peng_Wang:ECJ,
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author = "Peng Wang2 and Bing Xue and Jing Liang and
Mengjie Zhang",
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title = "Genetic Programming for Automatically Evolving
Multiple Features to Classification",
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journal = "Evolutionary Computation",
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note = "Just accepted",
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keywords = "genetic algorithms, genetic programming,
Classification, Dimensionality reduction",
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ISSN = "1063-6560",
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URL = "https://doi.org/10.1162/evco_a_00359",
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eprint = "https://direct.mit.edu/evco/article-pdf/doi/10.1162/evco_a_00359/2470769/evco_a_00359.pdf",
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DOI = "doi:10.1162/evco_a_00359",
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size = "27 pages",
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abstract = "Performing classification on high-dimensional data
poses a significant challenge due to the huge search
space. Moreover, complex feature interactions introduce
an additional obstacle. The problems can be addressed
by using feature selection to select relevant features
or feature construction to construct a small set of
high-level features. However, performing feature
selection or feature construction only might make the
feature set suboptimal. To remedy this problem, this
study investigates the use of genetic programming for
simultaneous feature selection and feature construction
in addressing different classification tasks. The
proposed approach is tested on 16 datasets and compared
with seven methods including both feature selection and
feature constructions techniques. The results show that
the obtained feature sets with the constructed and/or
selected features can significantly increase the
classification accuracy and reduce the dimensionality
of the datasets. Further analysis reveals the
complementarity of the obtained features leading to the
promising classification performance of the proposed
method.",
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notes = "School of Electrical and Information Engineering,
Zhengzhou University, Zhengzhou 450001, China",
- }
Genetic Programming entries for
Peng Wang2
Bing Xue
Jing Liang
Mengjie Zhang
Citations