- 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)",
