Using Self-Similarity to Adapt Evolutionary Ensembles for the Distributed Classification of Data Streams
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Pizzuti:2010:ICEC,
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author = "Clara Pizzuti and Giandomenico Spezzano",
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title = "Using Self-Similarity to Adapt Evolutionary Ensembles
for the Distributed Classification of Data Streams",
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booktitle = "Proceedings of the International Conference on
Evolutionary Computation (ICEC 2010)",
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year = "2010",
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editor = "Joaquim Filipe and Janusz Kacprzyk",
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pages = "176--181",
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address = "Valencia, Spain",
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month = "24-26 " # oct,
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organisation = "INSTICC, AAAI, WfMC",
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publisher = "SciTePress",
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keywords = "genetic algorithms, genetic programming, Co-evolution
and Collective Behaviour, Data mining, Classification,
Ensemble classifiers, Streaming data, Fractal
dimension",
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isbn13 = "978-989-8425-31-7",
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URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf",
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URL = "https://www.scitepress.org/PublishedPapers/2010/30749/",
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DOI = "doi:10.5220/0003074901760181",
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size = "6 pages",
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abstract = "Distributed stream-based classification methods have
many important applications such as sensor data
analysis, network security, and business intelligence.
An important challenge is to address the issue of
concept drift in the data stream environment, which is
not easily handled by the traditional learning
techniques. This paper presents a Genetic Programming
(GP) based boosting ensemble method for the
classification of distributed streaming data able to
adapt in presence of concept drift. 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. The algorithm
uses a fractal dimension-based change detection
strategy, based on self-similarity of the ensemble
behaviour, that permits the capture of time-evolving
trends and patterns in the stream, and to reveal
changes in evolving data streams. Experimental results
on a real life data set show the validity of the
approach in maintaining an accurate and up-to-date GP
ensemble.",
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notes = "Broken http://www.icec.ijcci.org/ICEC2010/home.asp
http://www.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm
Also known as \cite{DBLP:conf/ijcci/PizzutiS10}",
- }
Genetic Programming entries for
Clara Pizzuti
Giandomenico Spezzano
Citations