Handling Different Categories of Concept Drifts in Data Streams using Distributed GP
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Folino:2010:EuroGP,
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author = "Gianluigi Folino and Giuseppe Papuzzo",
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title = "Handling Different Categories of Concept Drifts in
Data Streams using Distributed GP",
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booktitle = "Proceedings of the 13th European Conference on Genetic
Programming, EuroGP 2010",
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year = "2010",
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editor = "Anna Isabel Esparcia-Alcazar and Aniko Ekart and
Sara Silva and Stephen Dignum and A. Sima Uyar",
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volume = "6021",
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series = "LNCS",
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pages = "74--85",
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address = "Istanbul",
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month = "7-9 " # apr,
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organisation = "EvoStar",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-3-642-12147-0",
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DOI = "doi:10.1007/978-3-642-12148-7_7",
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abstract = "Using Genetic Programming (GP) for classifying data
streams is problematic as GP is slow compared with
traditional single solution techniques. However, the
availability of cheaper and better-performing
distributed and parallel architectures make it possible
to deal with complex problems previously hardly solved
owing to the large amount of time necessary. This work
presents a general framework based on a distributed GP
ensemble algorithm for coping with different types of
concept drift for the task of classification of large
data streams. The framework is able to detect changes
in a very efficient way using only a detection function
based on the incoming unclassified data. Thus, only if
a change is detected a distributed GP algorithm is
performed in order to improve classification accuracy
and this limits the overhead associated with the use of
a population-based method. Real world data streams may
present drifts of different types. The introduced
detection function, based on the self-similarity
fractal dimension, permits to cope in a very short time
with the main types of different drifts, as
demonstrated by the first experiments performed on some
artificial datasets. Furthermore, having an adequate
number of resources, distributed GP can handle very
frequent concept drifts.",
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notes = "BoostCGPC, cellular GP, island model, AdaBoost,
Fractal dimension FD3, cloud computing, Minku Part of
\cite{Esparcia-Alcazar:2010:GP} EuroGP'2010 held in
conjunction with EvoCOP2010 EvoBIO2010 and
EvoApplications2010",
- }
Genetic Programming entries for
Gianluigi Folino
Giuseppe Papuzzo
Citations