GEEK: Grammatical Evolution for Automatically Evolving Kernel Functions
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{conf/trustcom/SousaLB17,
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title = "{GEEK}: Grammatical Evolution for Automatically
Evolving Kernel Functions",
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author = "Arua {De M. Sousa} and Ana Carolina Lorena and
Marcio P. Basgalupp",
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booktitle = "2017 IEEE Trustcom/BigDataSE/ICESS",
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publisher = "IEEE",
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year = "2017",
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pages = "941--948",
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month = "1-4 " # aug,
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address = "Sydney, Australia",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution",
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bibdate = "2017-09-15",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/trustcom/trustcom2017.html#SousaLB17",
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isbn13 = "978-1-5090-4906-6",
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URL = "http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8027145",
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DOI = "doi:10.1109/Trustcom/BigDataSE/ICESS.2017.334",
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abstract = "One of the key aspects in the successful use of kernel
methods such as Support Vector Machines is the proper
choice of the kernel function. While there are several
well known kernel functions which can produce
satisfactory results for various applications (e.g.
RBF), they do not take into account specific
characteristics of the data sets. Moreover, they have a
set of parameters to be tuned. In this paper, we
propose GEEK, a Grammatical Evolution approach for
automatically Evolving Kernel functions. GEEK uses a
grammar composed of simple mathematical operations
extracted from known kernels and is also able to
optimize some of their parameters. When combined
through the Grammatical Evolution, these operations
give rise to more complex kernel functions, adapted to
each specific problem in a data-driven approach. The
predictive results obtained by Support Vector Machines
using the GEEK kernel functions were in general
statistically similar to those of the standard RBF,
Polynomial and Sigmoid kernel functions, which had
their parameters optimized by a grid search method.
Nonetheless, the GEEK kernels were able to handle more
properly imbalanced classification problems, whilst the
results of the standard kernel functions were biased
towards the majority class.",
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notes = "also known as \cite{8029537}",
- }
Genetic Programming entries for
Arua De M Sousa
Ana Carolina Lorena
Marcio Porto Basgalupp
Citations