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

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

@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 Khanchi Andrew R McIntyre Malcolm Heywood Nur Zincir-Heywood

Citations