Visualisation of High Dimensional Data by Use of Genetic Programming: Application to On-line Infrared Spectroscopy Based Process Monitoring

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InCollection{Kulcsar:wsc17,
  author =       "Tibor Kulcsar and Gabor Bereznai and Gabor Sarossy and 
                 Robert Auer and Janos Abonyi",
  title =        "Visualisation of High Dimensional Data by Use of
                 Genetic Programming: Application to On-line Infrared
                 Spectroscopy Based Process Monitoring",
  booktitle =    "Soft Computing in Industrial Applications",
  publisher =    "Springer",
  year =         "2014",
  editor =       "Vaclav Snasel and Pavel Kroemer and Mario Koeppen and 
                 Gerald Schaefer",
  volume =       "223",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "223--231",
  month =        "21 " # nov,
  note =         "Proceedings of the 17th Online World Conference on
                 Soft Computing in Industrial Applications",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-00929-2",
  DOI =          "doi:10.1007/978-3-319-00930-8_20",
  abstract =     "In practical data mining and process monitoring
                 problems high-dimensional data has to be analysed. In
                 most of the cases it is very informative to map and
                 visualise the hidden structure of complex data in a
                 low-dimensional space. Industrial applications require
                 easily implementable, interpretable and accurate
                 projection. Nonlinear functions (aggregates) are useful
                 for this purpose. A pair of these functions realise
                 feature selection and transformation but finding the
                 proper model structure is a complex nonlinear
                 optimisation problem. We present a Genetic Programming
                 (GP) based algorithm to generate aggregates represented
                 in a tree structure. Results show that the developed
                 tool can be effectively used to build an on-line
                 spectroscopy based process monitoring system; the
                 two-dimensional mapping of high dimensional spectral
                 database can represent different operating ranges of
                 the process.",
  notes =        "http://dap.vsb.cz/wsc17/ WSC17 2012 Online Conference
                 on Soft Computing in Industrial Applications Anywhere
                 on Earth, December 10-21, 2012

                 Author Affiliations Department of Process Engineering,
                 University of Pannonia, Veszprem, H-8200, Hungary MOL
                 Ltd. Duna Refinery, Szazhalombatta, H-2440, Hungary",
}

Genetic Programming entries for Tibor Kulcsar Gabor Bereznai Gabor Sarossy Robert Auer Janos Abonyi

Citations