Genetic Programming for Performance Improvement and Dimensionality Reduction of Classification Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @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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keywords = "genetic algorithms, genetic programming",
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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 = "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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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