Dimension Reduction Using Evolutionary Support Vector Machines
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Ang:2008:cec,
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author = "J. H. Ang and E. J. Teoh and C. H. Tan and
K. C. Goh and K. C. Tan",
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title = "Dimension Reduction Using Evolutionary Support Vector
Machines",
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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 = "3634--3641",
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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 = "EC0777.pdf",
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DOI = "doi:10.1109/CEC.2008.4631290",
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abstract = "This paper presents a novel approach of hybridising
two conventional machine learning algorithms for
dimension reduction. Genetic Algorithm (GA) and Support
Vector Machines (SVMs) are integrated effectively based
on a wrapper approach. Specifically, the GA component
searches for the best attribute set using principles of
evolutionary process, after which the reduced dataset
is presented to the SVMs. Simulation results show that
GA-SVM hybrid is able to produce good classification
accuracy and a high level of consistency. In addition,
improvements are made to the hybrid by using a
correlation measure between attributes as a fitness
measure to replace the weaker members in the population
with newly formed chromosomes. This correlation measure
injects greater diversity and increases the overall
fitness of the population",
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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
Brian Ji Hua Ang
E J Teoh
Chin Hiong Tan
K C Goh
Kay Chen Tan
Citations