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

  • Tibor Kulcsar
  • Gabor Bereznai
  • Gabor Sarossy
  • Robert Auer
  • Janos Abonyi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 223)


In practical data mining and process monitoring problems high-dimensional data has to be analyzed. In most of the cases it is very informative to map and visualize 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.


Genetic programming Nonlinear data projection High dimensional data Visualisation 



The financial support of the TAMOP-4.2.2/B-10/1-2010-0025 and the TAMOP-4.2.2.A-11/1/KONV-2012-0071 projects are gratefully acknowledged.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tibor Kulcsar
    • 1
  • Gabor Bereznai
    • 2
  • Gabor Sarossy
    • 2
  • Robert Auer
    • 2
  • Janos Abonyi
    • 1
  1. 1.Department of Process EngineeringUniversity of PannoniaVeszpremHungary
  2. 2.MOL Ltd. Duna RefinerySzazhalombattaHungary

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