Feature Construction and Dimension Reduction Using Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{DBLP:conf/ausai/NeshatianZJ07,
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author = "Kourosh Neshatian and Mengjie Zhang and
Mark Johnston",
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title = "Feature Construction and Dimension Reduction Using
Genetic Programming",
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year = "2007",
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editor = "Mehmet A. Orgun and John Thornton",
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booktitle = "Proceedings of the 20th Australian Joint Conference on
Artificial Intelligence",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "4830",
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address = "Gold Coast, Australia",
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month = dec # " 2-6",
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pages = "160--170",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-540-76926-2",
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DOI = "doi:10.1007/978-3-540-76928-6_18",
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size = "11 pages",
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abstract = "This paper describes a new approach to the use of
genetic programming (GP) for feature construction in
classification problems. Rather than wrapping a
particular classifier for single feature construction
as in most of the existing methods, this approach uses
GP to construct multiple (high-level) features from the
original features. These constructed features are then
used by decision trees for classification. As feature
construction is independent of classification, the
fitness function is designed based on the class
dispersion and entropy. This approach is examined and
compared with the standard decision tree method, using
the original features, and using a combination of the
original features and constructed features, on 12
benchmark classification problems. The results show
that the new approach outperforms the standard way of
using decision trees on these problems in terms of the
classification performance, dimension reduction and the
learned decision tree size.",
- }
Genetic Programming entries for
Kourosh Neshatian
Mengjie Zhang
Mark Johnston
Citations