Mining Distributed Evolving Data Streams using Fractal GP Ensembles
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{eurogp07:folino,
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author = "Gianluigi Folino and Clara Pizzuti and
Giandomenico Spezzano",
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title = "Mining Distributed Evolving Data Streams using Fractal
GP Ensembles",
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editor = "Marc Ebner and Michael O'Neill and Anik\'o Ek\'art and
Leonardo Vanneschi and Anna Isabel Esparcia-Alc\'azar",
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booktitle = "Proceedings of the 10th European Conference on Genetic
Programming",
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publisher = "Springer",
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series = "Lecture Notes in Computer Science",
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volume = "4445",
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year = "2007",
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address = "Valencia, Spain",
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month = "11-13 " # apr,
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pages = "160--169",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "3-540-71602-5",
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isbn13 = "978-3-540-71602-0",
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DOI = "doi:10.1007/978-3-540-71605-1_15",
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abstract = "A Genetic Programming based boosting ensemble method
for the classification of distributed streaming data is
proposed. The approach handles flows of data coming
from multiple locations by building a global model
obtained by the aggregation of the local models coming
from each node. A main characteristics of the algorithm
presented is its adaptability in presence of concept
drift. Changes in data can cause serious deterioration
of the ensemble performance. Our approach is able to
discover changes by adopting a strategy based on
self-similarity of the ensemble behaviour, measured by
its fractal dimension, and to revise itself by promptly
restoring classification accuracy. Experimental results
on a synthetic data set show the validity of the
approach in maintaining an accurate and up-to-date GP
ensemble.",
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notes = "Part of \cite{ebner:2007:GP} EuroGP'2007 held in
conjunction with EvoCOP2007, EvoBIO2007 and
EvoWorkshops2007",
- }
Genetic Programming entries for
Gianluigi Folino
Clara Pizzuti
Giandomenico Spezzano
Citations