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.
CITATION STYLE
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 Advances in Intelligent Systems and Computing (Vol. 223, pp. 223–231). Springer Verlag. https://doi.org/10.1007/978-3-319-00930-8_20
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