SAX-EFG: an evolutionary feature generation framework for time series classification
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- @InProceedings{Kamath:2014:GECCO,
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author = "Uday Kamath and Jessica Lin and Kenneth {De Jong}",
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title = "SAX-EFG: an evolutionary feature generation framework
for time series classification",
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booktitle = "GECCO '14: Proceedings of the 2014 conference on
Genetic and evolutionary computation",
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year = "2014",
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editor = "Christian Igel and Dirk V. Arnold and
Christian Gagne and Elena Popovici and Anne Auger and
Jaume Bacardit and Dimo Brockhoff and Stefano Cagnoni and
Kalyanmoy Deb and Benjamin Doerr and James Foster and
Tobias Glasmachers and Emma Hart and Malcolm I. Heywood and
Hitoshi Iba and Christian Jacob and Thomas Jansen and
Yaochu Jin and Marouane Kessentini and
Joshua D. Knowles and William B. Langdon and Pedro Larranaga and
Sean Luke and Gabriel Luque and John A. W. McCall and
Marco A. {Montes de Oca} and Alison Motsinger-Reif and
Yew Soon Ong and Michael Palmer and
Konstantinos E. Parsopoulos and Guenther Raidl and Sebastian Risi and
Guenther Ruhe and Tom Schaul and Thomas Schmickl and
Bernhard Sendhoff and Kenneth O. Stanley and
Thomas Stuetzle and Dirk Thierens and Julian Togelius and
Carsten Witt and Christine Zarges",
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isbn13 = "978-1-4503-2662-9",
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pages = "533--540",
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keywords = "genetic algorithms, genetic programming",
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month = "12-16 " # jul,
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organisation = "SIGEVO",
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address = "Vancouver, BC, Canada",
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URL = "http://doi.acm.org/10.1145/2576768.2598321",
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DOI = "doi:10.1145/2576768.2598321",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "A variety of real world applications fit into the
broad definition of time series classification. Using
traditional machine learning approaches such as
treating the time series sequences as high dimensional
vectors have faced the well known curse of
dimensionality problem. Recently, the field of time
series classification has seen success by using
preprocessing steps that discretise the time series
using a Symbolic Aggregate ApproXimation technique
(SAX) and using recurring subsequences (motifs) as
features.
In this paper we explore a feature construction
algorithm based on genetic programming that uses
SAX-generated motifs as the building blocks for the
construction of more complex features. The research
shows that the constructed complex features improve the
classification accuracy in a statistically significant
manner for many applications.",
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notes = "Also known as \cite{2598321} GECCO-2014 A joint
meeting of the twenty third international conference on
genetic algorithms (ICGA-2014) and the nineteenth
annual genetic programming conference (GP-2014)",
- }
Genetic Programming entries for
Uday Kamath
Jessica Lin
Kenneth De Jong
Citations