Comparison between Genetic Programming and full model selection on classification problems
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gp-bibliography.bib Revision:1.8098
- @InProceedings{Valencia-Ramirez:2014:ROPEC,
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author = "J. M. Valencia-Ramirez and J. A. Raya and
J. R. Cedeno and R. R. Suarez and H. J. Escalante and M. Graff",
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booktitle = "IEEE International Autumn Meeting on Power,
Electronics and Computing (ROPEC 2014)",
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title = "Comparison between Genetic Programming and full model
selection on classification problems",
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year = "2014",
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month = nov,
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abstract = "Genetic Programming (GP) has been shown to be a
competitive classification technique. GP is generally
enhanced with a novel crossover, mutation, or selection
mechanism, in order to compare the performance of this
improvement with the performance of a standard GP.
Although these comparisons show the capabilities of GP,
it also makes harder, for a new comer, to figure out
whether a traditional GP would have a competitive
classification performance, when compared to
state-of-the-art techniques. In this work, we try to
fill this gap by comparing a standard GP, a GP with
minor modifications and a ensemble of GP with two
competitive techniques, namely support vector machines
and a procedure that performs full model selection
(Particle Swarm Model Selection). The results show that
GP has better performance on problems with high
dimensionality and large training sets and it is
competitive on the rest of the problems tested. The
former result is interesting because while Particle
Swarm Model Selection is tailored to perform a data
preprocessing and feature selection, GP is
automatically performing these tasks and producing
better classifiers.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/ROPEC.2014.7036349",
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notes = "Also known as \cite{7036349}",
- }
Genetic Programming entries for
Jose Maria Valencia-Ramirez
J A Raya
J R Cedeno
Ranyart Rodrigo Suarez
Hugo Jair Escalante
Mario Graff Guerrero
Citations