Abstract
Soft computing techniques are frequently used to develop data-driven prediction models. When modelling of an industrial process is planned, experiments in a real production environment are frequently required to collect the data. As a consequence, in many cases the experimental data sets contain only limited number of valuable records acquired in expensive experiments. This is accompanied by a relatively high number of measured variables. Hence, the need for dimensionality reduction of many industrial data sets.
The primary objective of this study is to experimentally assess one of the most popular approaches based on the use of principal component analysis and multilayer perceptrons. The way the reduced dimension could be determined is investigated. A method aiming to control the dimensionality reduction process in view of model prediction error is evaluated. The proposed method is tested on two industrial data sets. The prediction improvement arising from the proposed technique is discussed.
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Grzenda, M. (2011). Prediction-Oriented Dimensionality Reduction of Industrial Data Sets. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_24
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DOI: https://doi.org/10.1007/978-3-642-21822-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21821-7
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