Event Detection in Time Series by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Xie:2012:CEC,
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title = "Event Detection in Time Series by Genetic
Programming",
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author = "Feng Xie and Andy Song and Vic Ciesielski",
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pages = "2507--2514",
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booktitle = "Proceedings of the 2012 IEEE Congress on Evolutionary
Computation",
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year = "2012",
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editor = "Xiaodong Li",
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month = "10-15 " # jun,
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DOI = "doi:10.1109/CEC.2012.6256589",
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address = "Brisbane, Australia",
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ISBN = "0-7803-8515-2",
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keywords = "genetic algorithms, genetic programming,
Classification, clustering, data analysis and data
mining",
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abstract = "The aim of event detection in time series is to
identify particular occurrences of user-interest in one
or more time lines, such as finding an anomaly in
electrocardiograms or reporting a sudden variation of
voltage in a power supply. Current methods are not
adequate for detecting certain kinds of events without
any domain knowledge. Therefore, we propose a Genetic
Programming (GP) based event detection methodology in
which solutions can be built from raw time series data.
The framework is applied to five synthetic data sets
and one real world application. The experimental
results show that working on raw data even with a
dimensionality as high as 140 by 80, genetic
programming can achieve superior performance to
conventional methods operating on pre-defined features.
Furthermore, analysis of the evolved event detectors
shows that they can be readily understood by humans and
have captured the regularities inserted into the
synthetic data sets.",
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notes = "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
EPS and the IET.",
- }
Genetic Programming entries for
Feng Xie
Andy Song
Victor Ciesielski
Citations