Evolutionary computation based nonlinear transformations to low dimensional spaces for sensor data fusion and Visual Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Valdes:2010:cec,
  author =       "Julio J. Valdes",
  title =        "Evolutionary computation based nonlinear
                 transformations to low dimensional spaces for sensor
                 data fusion and Visual Data Mining",
  booktitle =    "IEEE Congress on Evolutionary Computation (CEC 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, DE",
  isbn13 =       "978-1-4244-6910-9",
  abstract =     "Data fusion approaches are nowadays needed and also a
                 challenge in many areas, like sensor systems monitoring
                 complex processes. This paper explores evolutionary
                 computation approaches to sensor fusion based on
                 unsupervised nonlinear transformations between the
                 original sensor space (possibly highly-dimensional) and
                 lower dimensional spaces. Domain-independent implicit
                 and explicit transformations for Visual Data Mining
                 using Differential Evolution and Genetic Programming
                 aiming at preserving the similarity structure of the
                 observed multivariate data are applied and compared
                 with classical deterministic methods. These approaches
                 are illustrated with a real world complex problem:
                 Failure conditions in Auxiliary Power Units in
                 aircraft. The results indicate that the evolutionary
                 approaches used were useful and effective at reducing
                 dimensionality while preserving the similarity
                 structure of the original data. Moreover the explicit
                 models obtained with Genetic Programming simultaneously
                 covered both feature selection and generation. The
                 evolutionary techniques used compared very well with
                 their classical counterparts, having additional
                 advantages. The transformed spaces also help in
                 visualising and understanding the properties of the
                 sensor data.",
  DOI =          "doi:10.1109/CEC.2010.5585951",
  notes =        "WCCI 2010. Also known as \cite{5585951}",
}

Genetic Programming entries for Julio J Valdes

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