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.
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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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Kulcsar, T., Bereznai, G., Sarossy, G., Auer, R., Abonyi, J. (2014). Visualisation of High Dimensional Data by Use of Genetic Programming: Application to On-line Infrared Spectroscopy Based Process Monitoring. In: Snášel, V., Krömer, P., Köppen, M., Schaefer, G. (eds) Soft Computing in Industrial Applications. Advances in Intelligent Systems and Computing, vol 223. Springer, Cham. https://doi.org/10.1007/978-3-319-00930-8_20
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DOI: https://doi.org/10.1007/978-3-319-00930-8_20
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