Genetic Programming of Heterogeneous Ensembles for Classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{conf/ciarp/EscalanteAMA13,
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author = "Hugo Jair Escalante and Niusvel Acosta-Mendoza and
Alicia Morales-Reyes and Andres Gago Alonso",
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title = "Genetic Programming of Heterogeneous Ensembles for
Classification",
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year = "2013",
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booktitle = "Proceedings of the 18th Iberoamerican Congress on
Image Analysis, Computer Vision, and Applications
(CIARP 2013) Part {I}",
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editor = "Jose Ruiz-Shulcloper and Gabriella Sanniti di Baja",
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volume = "8258",
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series = "Lecture Notes in Computer Science",
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pages = "9--16",
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address = "Havana, Cuba",
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month = nov # " 20-23",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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bibdate = "2013-11-17",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/ciarp/ciarp2013-1.html#EscalanteAMA13",
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isbn13 = "978-3-642-41821-1",
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URL = "http://dx.doi.org/10.1007/978-3-642-41822-8",
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size = "8 pages",
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abstract = "The ensemble classification paradigm is an effective
way to improve the performance and stability of
individual predictors. Many ways to build ensembles
have been proposed so far, most notably bagging and
boosting based techniques. Evolutionary algorithms
(EAs) also have been widely used to generate ensembles.
In the context of heterogeneous ensembles EAs have been
successfully used to adjust weights of base classifiers
or to select ensemble members. Usually, a weighted sum
is used for combining classifiers outputs in both
classical and evolutionary approaches. This study
proposes a novel genetic program that learns a fusion
function for combining heterogeneous-classifiers
outputs. It evolves a population of fusion functions in
order to maximise the classification accuracy. Highly
non-linear functions are obtained with the proposed
method, subsuming the existing weighted-sum
formulations. Experimental results show the
effectiveness of the proposed approach, which can be
used not only with heterogeneous classifiers but also
with homogeneous-classifiers and under bagging/boosting
based formulations.",
- }
Genetic Programming entries for
Hugo Jair Escalante
Niusvel Acosta-Mendoza
Alicia Morales-Reyes
Andres Gago Alonso
Citations