Using Self-Similarity to Adapt Evolutionary Ensembles for the Distributed Classification of Data Streams

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Pizzuti:2010:ICEC,
  author =       "Clara Pizzuti and Giandomenico Spezzano",
  title =        "Using Self-Similarity to Adapt Evolutionary Ensembles
                 for the Distributed Classification of Data Streams",
  booktitle =    "Proceedings of the International Conference on
                 Evolutionary Computation (ICEC 2010)",
  year =         "2010",
  editor =       "Joaquim Filipe and Janusz Kacprzyk",
  pages =        "176--181",
  address =      "Valencia, Spain",
  month =        "24-26 " # oct,
  organisation = "INSTICC, AAAI, WfMC",
  publisher =    "SciTePress",
  keywords =     "genetic algorithms, genetic programming, Co-evolution
                 and Collective Behaviour, Data mining, Classification,
                 Ensemble classifiers, Streaming data, Fractal
                 dimension",
  isbn13 =       "978-989-8425-31-7",
  URL =          "http://www.robinbye.com/files/publications/ICEC_2010.pdf",
  DOI =          "doi:10.5220/0003074901760181",
  size =         "6 pages",
  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.",
  notes =        "http://www.icec.ijcci.org/ICEC2010/home.asp
                 http://www.ecta.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm
                 Also known as \cite{DBLP:conf/ijcci/PizzutiS10}",
}

Genetic Programming entries for Clara Pizzuti Giandomenico Spezzano

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