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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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",
• URL = "https://www.scitepress.org/PublishedPapers/2010/30749/",
• 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 = "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

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