SAX-EFG: an evolutionary feature generation framework for time series classification

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@InProceedings{Kamath:2014:GECCO,
  author =       "Uday Kamath and Jessica Lin and Kenneth {De Jong}",
  title =        "SAX-EFG: an evolutionary feature generation framework
                 for time series classification",
  booktitle =    "GECCO '14: Proceedings of the 2014 conference on
                 Genetic and evolutionary computation",
  year =         "2014",
  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",
  isbn13 =       "978-1-4503-2662-9",
  pages =        "533--540",
  keywords =     "genetic algorithms, genetic programming",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2576768.2598321",
  DOI =          "doi:10.1145/2576768.2598321",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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.",
  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