Label free change detection on streaming data with cooperative multi-objective genetic programming

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@InProceedings{Rahimi:2013:GECCOcomp,
  author =       "Sara Rahimi and Andrew R. McIntyre and 
                 Malcolm I. Heywood and Nur Zincir-Heywood",
  title =        "Label free change detection on streaming data with
                 cooperative multi-objective genetic programming",
  booktitle =    "GECCO '13 Companion: Proceeding of the fifteenth
                 annual conference companion on Genetic and evolutionary
                 computation conference companion",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and 
                 Thomas Bartz-Beielstein and Daniele Loiacono and 
                 Francisco Luna and Joern Mehnen and Gabriela Ochoa and 
                 Mike Preuss and Emilia Tantar and Leonardo Vanneschi and 
                 Kent McClymont and Ed Keedwell and Emma Hart and 
                 Kevin Sim and Steven Gustafson and 
                 Ekaterina Vladislavleva and Anne Auger and Bernd Bischl and Dimo Brockhoff and 
                 Nikolaus Hansen and Olaf Mersmann and Petr Posik and 
                 Heike Trautmann and Muhammad Iqbal and Kamran Shafi and 
                 Ryan Urbanowicz and Stefan Wagner and 
                 Michael Affenzeller and David Walker and Richard Everson and 
                 Jonathan Fieldsend and Forrest Stonedahl and 
                 William Rand and Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton and Gisele L. Pappa and 
                 John Woodward and Jerry Swan and Krzysztof Krawiec and 
                 Alexandru-Adrian Tantar and Peter A. N. Bosman and 
                 Miguel Vega-Rodriguez and Jose M. Chaves-Gonzalez and 
                 David L. Gonzalez-Alvarez and 
                 Sergio Santander-Jimenez and Lee Spector and Maarten Keijzer and 
                 Kenneth Holladay and Tea Tusar and Boris Naujoks",
  isbn13 =       "978-1-4503-1964-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "159--160",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2464576.2464652",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Classification under streaming data conditions
                 requires that the machine learning (ML) approach
                 operate interactively with the stream content. Thus,
                 given some initial ML classification capability, it is
                 not possible to assume that stream content will be
                 stationary. It is therefore necessary to first detect
                 when the stream content changes. Only after detecting a
                 change, can classifier retraining be triggered. Current
                 methods for change detection tend to assume an entropy
                 filter approach, where class labels are necessary. In
                 practice, labelling the stream would be extremely
                 expensive. This work proposes an approach in which the
                 behaviour of GP individuals is used to detect change
                 without the use of labels. Only after detecting a
                 change is label information requested. Benchmarking
                 under a computer network traffic analysis scenario
                 demonstrates that the proposed approach performs at
                 least as well as the filter method, while retaining the
                 advantage of requiring no labels.",
  notes =        "Also known as \cite{2464652} Distributed at
                 GECCO-2013.",
}

Genetic Programming entries for Sara Rahimi Andrew R McIntyre Malcolm Heywood Nur Zincir-Heywood

Citations