Genetic programming and serial processing for time series classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Eva Alfaro-Cid and Ken Sharman and 
                 Anna I. Esparcia-Alcazar",
  title =        "Genetic programming and serial processing for time
                 series classification",
  journal =      "Evolutionary Computation",
  year =         "2014",
  volume =       "22",
  number =       "2",
  pages =        "265--285",
  month =        "Summer",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, time series, serial data processing,
                 real world applications",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/EVCO_a_00110",
  size =         "20 pages",
  abstract =     "This work describes an approach devised by the authors
                 for time series classification. In our approach genetic
                 programming is used in combination with a serial
                 processing of data, where the last output is the result
                 of the classification. The use of genetic programming
                 for classification, although still a field where more
                 research in needed, is not new. However, the
                 application of genetic programming to classification
                 tasks is normally done by considering the input data as
                 a feature vector. That is, to the best of our
                 knowledge, there are not examples in the genetic
                 programming literature of approaches where the time
                 series data are processed serially and the last output
                 is considered as the classification result. The serial
                 processing approach presented here fills a gap in the
                 existing literature. This approach was tested in three
                 different problems. Two of them are real world problems
                 whose data were gathered for on-line or conference
                 competitions. As there are published results of these
                 two problems this gives us the chance of comparing the
                 performance of our approach against top performing
                 methods. The serial processing of data in combination
                 with genetic programming obtained competitive results
                 in both competitions, showing its potential for solving
                 time series classification problems. The main advantage
                 of our serial processing approach is that it can easily
                 handle very large data sets.",
  notes =        "ECJ. EEG BCI competition II. Ford Classification

Genetic Programming entries for Eva Alfaro-Cid Kenneth C Sharman Anna Esparcia-Alcazar