Learning patterns of states from multi-channel time series using genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Song:2016:SC,
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author = "Andy Song and Feng Xie and Vic Ciesielski",
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title = "Learning patterns of states from multi-channel time
series using genetic programming",
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journal = "Soft Computing",
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year = "2016",
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volume = "20",
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number = "10",
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pages = "3915--3925",
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keywords = "genetic algorithms, genetic programming, Pattern
learning, Time series, States, Multi-channel time
series",
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ISSN = "1433-7479",
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DOI = "doi:10.1007/s00500-016-2127-9",
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size = "11 pages",
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abstract = "A state in time series is time series data stream
maintaining a certain pattern over a period of time,
for example, holding a steady value, being above a
certain threshold and oscillating regularly. Automatic
learning and discovery of these patterns of time series
states can be useful in a range of scenarios of
monitoring and classifying stream data, for example,
activity recognition based on body sensor readings. In
this study, we present our genetic programming
(GP)-based time series analysis method on learning
various types of states from multi-channel data
streams. This evolutionary learning method can handle
relatively complex scenarios using only raw input. This
method does not require prior knowledge of the
relationships between channels. It does not require
manually defined feature to be constructed. The
evaluation using both artificial and real-world
multi-channel time series data shows that this method
on raw input can outperform classic learning methods on
pre-defined features. The analysis shows patterns can
be discovered by the GP method.",
- }
Genetic Programming entries for
Andy Song
Feng Xie
Victor Ciesielski
Citations