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)

Abstract

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.

Keywords

Genetic programming Nonlinear data projection High dimensional data Visualisation 

Notes

Acknowledgments

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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