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Prediction-Oriented Dimensionality Reduction of Industrial Data Sets

  • Maciej Grzenda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6703)

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.

Keywords

Dimensionality Reduction Soft Computing Technique Nonlinear Dimensionality Reduction Tooth Passing Frequency Prediction Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Grzenda, M., Bustillo, A., Zawistowski, P.: A Soft Computing System Using Intelligent Imputation Strategies for Roughness Prediction in Deep Drilling. Journal of Intelligent Manufacturing, 1–11 (2010), http://dx.doi.org/10.1007/s10845-010-0478-0
  2. 2.
    Haykin, S.: Neural Networks and Learning Machines. Person Education (2009)Google Scholar
  3. 3.
    Kegl, B.: Intrinsic Dimension Estimation Using Packing Numbers. In: Adv. In Neural Inform. Proc. Systems, Massachusetts Inst. of Technology, vol. 15, pp. 697–704 (2003)Google Scholar
  4. 4.
    Larose, D.T.: Data Mining Methods and Models. John Wiley & Sons, Chichester (2006)zbMATHGoogle Scholar
  5. 5.
    Lee, J., Verleysen, M.: Nonlinear Dimensionality Reduction. Springer, Heidelberg (2010)zbMATHGoogle Scholar
  6. 6.
    Li, D.-C., et al.: A Non-linear Quality Improvement Model Using SVR for Manufacturing TFT-LCDs. Journal of Intelligent Manufacturing, 1–10 (2010)Google Scholar
  7. 7.
    Maszczyk, T., Duch, W.: Support Vector Machines for Visualization and Dimensionality Reduction. In: Kůrková, V., Neruda, R., Koutník, J. (eds.) ICANN 2008, Part I. LNCS, vol. 5163, pp. 346–356. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Pal, S., et al.: Tool Wear Monitoring and Selection of Optimum Cutting Conditions with Progressive Tool Wear Effect and Input Uncertainties. Journal of Intelligent Manufacturing, 1–14 (2009)Google Scholar
  9. 9.
    Redondo, R., Santos, P., Bustillo, A., Sedano, J., Villar, J.R., Correa, M., Alique, J.R., Corchado, E.: A Soft Computing System to Perform Face Milling Operations. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 1282–1291. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Rosman, G., et al.: Nonlinear Dimensionality Reduction by Topologically Constrained Isometric Embedding. Int. J. of Computer Vision 89(1), 56–68 (2010)CrossRefGoogle Scholar
  11. 11.
    Verbeke, N., Vincent, N.: A PCA-Based Technique to Detect Moving Objects. In: Ersbøll, B.K., Pedersen, K.S. (eds.) SCIA 2007. LNCS, vol. 4522, pp. 641–650. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Maciej Grzenda
    • 1
  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarszawaPoland

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