GP ensembles for large-scale data classification
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{Folino:2005:ieeeTEC,
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author = "Gianluigi Folino and Clara Pizzuti and
Giandomenico Spezzano",
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title = "GP ensembles for large-scale data classification",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2006",
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volume = "10",
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number = "5",
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pages = "604--616",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Bagging,
boosting, classification, data mining",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2005.863627",
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size = "13 pages",
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abstract = "An extension of cellular genetic programming for data
classification (CGPC) to induce an ensemble of
predictors is presented. Two algorithms implementing
the bagging and boosting techniques are described and
compared with CGPC. The approach is able to deal with
large data sets that do not fit in main memory since
each classifier is trained on a subset of the overall
training data. The predictors are then combined to
classify new tuples. Experiments on several data sets
show that, by using a training set of reduced size,
better classification accuracy can be obtained, but at
a much lower computational cost",
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notes = "Also known as \cite{1705406}",
- }
Genetic Programming entries for
Gianluigi Folino
Clara Pizzuti
Giandomenico Spezzano
Citations