Label free change detection on streaming data with cooperative multi-objective genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Rahimi:2013:GECCOcomp,
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author = "Sara Rahimi and Andrew R. McIntyre and
Malcolm I. Heywood and Nur Zincir-Heywood",
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title = "Label free change detection on streaming data with
cooperative multi-objective genetic programming",
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booktitle = "GECCO '13 Companion: Proceeding of the fifteenth
annual conference companion on Genetic and evolutionary
computation conference companion",
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year = "2013",
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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",
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isbn13 = "978-1-4503-1964-5",
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keywords = "genetic algorithms, genetic programming",
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pages = "159--160",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2464576.2464652",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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 Khanchi
Andrew R McIntyre
Malcolm Heywood
Nur Zincir-Heywood
Citations