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Springer Handbook of Computational Intelligence pp 1143–1157Cite as

Computational Intelligence in Industrial Applications

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Abstract

In this chapter, we review the progress and the impact of computational intelligence for industrial applications sampled from the last 10 years of our personal careers and areas of research (all authors of this chapter do computational modeling for a living). This chapter is structured as follows. Section 57.2 introduces a classification of data-driven predictive analytics problems into three groups based on the goals and the information content of the data. Section 57.3 briefly covers most frequently used methods for predictive modeling and compares them in the context of available a priori knowledge and required execution time. Section 57.4 focuses on the importance of good workflows for successful predictive analytics projects. Section 57.5 provides several examples of such workflows. Section 57.6 concludes the chapter.

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Abbreviations

CI:

computational intelligence

SVM:

support vector machine

References

  1. M. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburgh, A. H. Byers: Big data: The next frontier for innovation, competition, and productivity, available online at http://www.mckinsey.com/mgi (2011)

  2. T.H. Davenport, J.G. Harris: Competing on Analytics: The New Science of Winning, 1st edn. (Harvard Business School, Boston 2007)

    Google Scholar 

  3. J.C. Torfs, G.J. Brands, E.G. Goethals, E.M. Dedeyne: Method for characterizing the appearance of a particular object, for predicting the appearance of an object, and for manufacturing an object having a predetermined appearance, which has optionally been determined on a basis of a reference object, WO Patent Ser 2002042750 A1 (2004)

    Google Scholar 

  4. R.A. Johnson, D.W. Wichern: Applied Multivariate Statistical Analysis (Prentice Hall, Englewood Cliffs 1988)

    MATH  Google Scholar 

  5. L. Breiman: Random forests, Mach. Learn. 45, 5–32 (2001)

    Article  MATH  Google Scholar 

  6. M.D.J. Powell: Radial basis functions for multivariable interpolation: A review. In: Algorithms for Approximation, ed. by J. Mason, M.G. Cox (Clarendon, Oxford 1987) pp. 143–167

    Google Scholar 

  7. S. Haykin: Neural Networks and Learning Machines, 3rd edn. (Pearson Educ., Harlow 2008)

    Google Scholar 

  8. V. Vapnik: Estimation of Dependences Based on Empirical Data (Springer, Berlin, Heidelberg 1982)

    MATH  Google Scholar 

  9. V. Vapnik: The support vector method, Proc. 7th Int. Conf. Artif. Neural Netw. (1997) pp. 263–271

    Google Scholar 

  10. J.R. Koza: Genetic Programming: On the Programming of Computers by Means of Natural Selection (MIT, Cambridge 1992)

    MATH  Google Scholar 

  11. R. Poli, W.B. Langdon, N.F. McPhee: A Field Guide to Genetic Programming (Lulu, Raleigh 2008)

    Google Scholar 

  12. A.K. Kordon: Applying Computational Intelligence: How to Create Value (Springer, Berlin, Heidelberg 2010)

    Book  Google Scholar 

  13. W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone: Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and its Applications (Morgan Kaufmann, San Francisco 1998)

    Book  MATH  Google Scholar 

  14. M. Kotanchek: Evolved Analytics LLC: DataModeler Release 8.0 (Evolved Analytics LLC, Midland 2010)

    Google Scholar 

  15. E. Vladislavleva: Model-based Problem Solving through Symbolic Regression via Pareto Genetic Programming (Tilburg Univ., Tilburg 2008)

    Google Scholar 

  16. S. Stijven, W. Minnebo, K. Vladislavleva: Separating the wheat from the chaff: On feature selection and feature importance in regression random forests and symbolic regression, Proc. 13th Annu. Conf. Companion Genet. Evol. Comput. (2011) pp. 623–630

    Google Scholar 

  17. S. Emmott, S.Rison: Towards 2020 Science, Microsoft, Cambridge (2006)

    Google Scholar 

  18. A.K. Kordon, G.F. Smits: Soft sensor development using genetic programming, Proc. Genet. Evolut. Comput. Conf. (2001) pp. 1346–1351

    Google Scholar 

  19. A.K. Kordon, G.F. Smits, A.N. Kalos, E.M. Jordaan: Robust soft sensor development using genetic programming. In: Nature-Inspired Methods in Chemometrics: Genetic Algorithm and Artificial Neural Networks, ed. by R. Leardi (Elsevier, Amsterdam 2003) pp. 69–108

    Chapter  Google Scholar 

  20. M. Kotanchek: Real-world data modeling, Proc. 12th Annu. Conf. Companion Genet. Evol. Comput. (2010) pp. 2863–2896

    Google Scholar 

  21. K. Veeramachaneni, E. Vladislavleva, U.-M. O'Reilly: Knowledge mining sensory evaluation data: Genetic programming, statistical techniques, and swarm optimization, Genet. Progr. Evol. Mach. 13(1), 103–133 (2012)

    Article  Google Scholar 

  22. K. Vladislavleva, K. Veeramachaneni, U.-M. O'Reilly: Learning a lot from only a little: Genetic programming for panel segmentation on sparse sensory evaluation data, Proc. 13th Eur. Conf. Genet. Progr. (2010) pp. 244–255

    Google Scholar 

  23. N. Staelens, D. Deschrijver, E. Vladislavleva, B. Vermeulen, T. Dhaene, P. Demeester: Constructing a no-reference H.264/AVC bitstream-based video quality metric using genetic programming-based symbolic regression, IEEE Trans. Circuits Syst. Video Technol. 23(8), 1322–1333 (2013)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Evolved Analytics Europe BVBA, A. Coppenslaan 27, 2300, Turnhout, Belgium

    Ekaterina Vladislavleva

  2. Core R&D, Dow Benelux BV, Herbert H. Dowweg 5, 4542, NM Hoek, Netherlands

    Guido Smits

  3. Evolved Analytics LLC, 3411 Valley Drive, MI 48640, Midland, USA

    Mark Kotanchek

Authors
  1. Ekaterina Vladislavleva
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  2. Guido Smits
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  3. Mark Kotanchek
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Corresponding author

Correspondence to Ekaterina Vladislavleva .

Editor information

Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Vladislavleva, E., Smits, G., Kotanchek, M. (2015). Computational Intelligence in Industrial Applications. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_57

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

  • eBook Packages: EngineeringEngineering (R0)

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