A Bayesian Approach for Combining Ensembles of GP Classifiers
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{DeStefano:2011:MCS,
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author = "C. {De Stefano} and F. Fontanella and G. Folino and
A. Scotto {di Freca}",
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title = "A {Bayesian} Approach for Combining Ensembles of {GP}
Classifiers",
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booktitle = "Multiple Classifier Systems",
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year = "2011",
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editor = "Carlo Sansone and Josef Kittler and Fabio Roli",
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volume = "6713",
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series = "LNCS",
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pages = "26--35",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-21557-5",
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DOI = "doi:10.1007/978-3-642-21557-5_5",
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size = "10 pages",
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abstract = "Recently, ensemble techniques have also attracted the
attention of Genetic Programming (GP) researchers. The
goal is to further improve GP classification
performances. Among the ensemble techniques, also
bagging and boosting have been taken into account.
These techniques improve classification accuracy by
combining the responses of different classifiers by
using a majority vote rule. However, it is really hard
to ensure that classifiers in the ensemble be
appropriately diverse, so as to avoid correlated
errors. Our approach tries to cope with this problem,
designing a framework for effectively combine GP-based
ensemble by means of a Bayesian Network. The proposed
system uses two different approaches. The first one
applies a boosting technique to a GP-based
classification algorithm in order to generate an
effective decision trees ensemble. The second module
uses a Bayesian network for combining the responses
provided by such ensemble and select the most
appropriate decision trees. The Bayesian network is
learned by means of a specifically devised Evolutionary
algorithm. Preliminary experimental results confirmed
the effectiveness of the proposed approach.",
- }
Genetic Programming entries for
Claudio De Stefano
Francesco R Fontanella
Gianluigi Folino
Alessandra Scotto di Freca
Citations