Genetic Programming for Performance Improvement and Dimensionality Reduction of Classification Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.5776
- @InProceedings{Neshatian:2008:cec,
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author = "Kourosh Neshatian and Mengjie Zhang",
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title = "Genetic Programming for Performance Improvement and
Dimensionality Reduction of Classification Problems",
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booktitle = "2008 IEEE World Congress on Computational
Intelligence",
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year = "2008",
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editor = "Jun Wang",
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pages = "2811--2818",
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address = "Hong Kong",
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month = "1-6 " # jun,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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isbn13 = "978-1-4244-1823-7",
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file = "EC0631.pdf",
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DOI = "
doi:10.1109/CEC.2008.4631175",
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abstract = "In this paper, Genetic programming (GP) is used to
construct a new set of high level features based on the
original attributes of a classification problem with
the goal of improving the classification performance
and reducing the dimensionality. A non-wrapper approach
is taken and a new fitness function is proposed based
on the Renyi entropy. The GP system uses a variable
terminal pool which is constructed by the class-wise
orthogonal transformations of the original features.
The performance measure is classification accuracy on
12 benchmark problems using constructed features in a
decision tree classifier. The performance over
difficult problems has been improved by constructing
features for compound classes. This approach is
compared with the principle component analysis (PCA)
method and the results show that the new approach
outperforms the PCA method on most of the problems in
terms of classification performance and dimensionality
reduction.",
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keywords = "genetic algorithms, genetic programming",
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notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
EPS and the IET.",
- }
Genetic Programming entries for
Kourosh Neshatian
Mengjie Zhang
Citations