, Volume 38, Issue 6, pp 500–509 | Cite as

From knowledge-based to data-driven fuzzy modeling

Development, criticism, and alternative directions
  • Eyke HüllermeierEmail author


This paper elaborates on a development in (applied) fuzzy logic that has taken place in the last couple of decades, namely, the complementation or even replacement of the traditional knowledge-based approach to fuzzy rule-based systems design by a data-driven one. It is argued that the classical rule-based modeling paradigm is actually more amenable to the knowledge-based approach, for which it was originally conceived, and less so to data-driven model design. An important reason that prevents fuzzy (rule-based) systems from being leveraged in large-scale applications is the flat structure of rule bases, along with the local nature of fuzzy rules and their limited ability to express complex dependencies between variables. As an alternative approach to fuzzy systems modeling, we advocate so-called fuzzy pattern trees. Because of its hierarchical, modular structure and the use of different types of (nonlinear) aggregation operators, a fuzzy pattern tree has the ability to represent functional dependencies in a more flexible and more compact way.


Fuzzy Logic Fuzzy System Fuzzy Rule Fuzzy Modeling Aggregation Function 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Angelov P, Filev D, Kasabov N (2010) Evolving Intelligent Systems. John Wiley and Sons, New YorkCrossRefGoogle Scholar
  2. 2.
    Babuska R (1998) Fuzzy Modeling for Control. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
  3. 3.
    Bengio Y (2009) Learning deep architectures for AI. Foundations Trends Machine Learning 2(1):1–127zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Cordon O, del Jesus MJ, Herrera F (1998) Analyzing the reasoning mechanisms in fuzzy rule based classification systems. Mathware Soft Comput 5:321–332zbMATHGoogle Scholar
  5. 5.
    Cordon O, Gomide F, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Set Syst 141(1):5–31zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Dubois D, Prade H (1980) New results about properties and semantics of fuzzy set-theoretic operators. In: Wang PP, Chang SK (eds) Fuzzy Sets: Theory and Applications to Policy Analysis and Information Systems, Plenum Press, New YorkGoogle Scholar
  7. 7.
    Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: an overview. In: Advances in Knowledge Discovery and Data Mining. MIT Press, pp 1–34Google Scholar
  8. 8.
    Grabisch M, Marichal JL, Mesiar R, Pap E (2009) Aggregation Functions. Cambridge University PressGoogle Scholar
  9. 9.
    Huang Z, Gedeon TD, Nikravesh M (2008) Pattern tree induction: a new machine learning method. IEEE T Fuzzy Syst 16(4):958–970CrossRefGoogle Scholar
  10. 10.
    Hüllermeier E (2005) Fuzzy sets in machine learning and data mining: Status and prospects. Fuzzy Set Syst 156(3):387–406CrossRefGoogle Scholar
  11. 11.
    Hüllermeier E (2011) Fuzzy machine learning and data mining. WIREs Data Min Knowl Disc 1(4):269–283CrossRefGoogle Scholar
  12. 12.
    Jang JSR (1993) ANFIS: Adaptive-network-based fuzzy inference systems. IEEE T Syst Man Cyb 23:665–685, 1993CrossRefGoogle Scholar
  13. 13.
    Jin Y (2000) Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE T Fuzzy Syst 8(2):212–221Google Scholar
  14. 14.
    Klement EP, Mesiar R, Pap E (2002) Triangular Norms. Kluwer Academic PublishersGoogle Scholar
  15. 15.
    Mamdani E, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7:1–13zbMATHCrossRefGoogle Scholar
  16. 16.
    Nasiri M, Fober T, Senge R, Hüllermeier E (2013) Fuzzy pattern trees as an alternative to rule-based fuzzy systems: knowledge-driven, data-driven and hybrid modeling of color yield in polyester dyeing. In: Proceedings IFSA–2013, World Congress of the International Fuzzy Systems Association, Edmonton, Canada, pp 715–721, 2013Google Scholar
  17. 17.
    Nasiri M, Hüllermeier E, Senge R, Lughofer E (2011) Comparing methods for knowledge-driven and data-driven fuzzy modeling: A case study in textile industry. In: Proceedings IFSA–2011, World Congress of the International Fuzzy Systems Association, Surabaya and Bali Island, Indonesia, pp RW-103-1–6, 2011Google Scholar
  18. 18.
    Nauck D, Klawonn F, Kruse R (1997) Foundations of Neuro-Fuzzy Systems. John Wiley and Sons, Chichester, UKGoogle Scholar
  19. 19.
    Passino KM, Yurkovich S (1998) Fuzzy Control. Addison-WesleyGoogle Scholar
  20. 20.
    Saaty TL (1980) The Analytic Hierarchy Process. McGraw-HillGoogle Scholar
  21. 21.
    Senge R, Fober T, Nasiri N, Hüllermeier E (2012) Fuzzy Pattern Trees: ein alternativer Ansatz zur Fuzzy-Modellierung. at – Atomatisierungstechnik 60(10):622–629CrossRefGoogle Scholar
  22. 22.
    Senge R, Hüllermeier E (2010) Pattern trees for regression and fuzzy systems modeling. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ)Google Scholar
  23. 23.
    Senge R, Hüllermeier E (2011) Top-down induction of fuzzy pattern trees. IEEE T Fuzzy Syst 19(2):241–252CrossRefGoogle Scholar
  24. 24.
    Senge R, Hüllermeier E (2015) Fast fuzzy pattern tree learning. IEEE T Fuzzy Syst PP(99):1Google Scholar
  25. 25.
    Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE T Syst Man Cyb 15(1):116–132zbMATHCrossRefGoogle Scholar
  26. 26.
    Torra V (2002) A review on the construction of hierarchical fuzzy systems. Int J Intell Syst 17(5):531–543zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Yager RR (1988) On ordered weighted averaging aggregation operators in multi criteria decision making. IEEE T Syst Man Cyb 18(1):183–190, 1988zbMATHCrossRefMathSciNetGoogle Scholar
  28. 28.
    Yi Y, Fober T, Hüllermeier E (2009) Fuzzy operator trees for modeling rating functions. Int J Comput Intell Appl 8(4):413–428zbMATHCrossRefGoogle Scholar
  29. 29.
    Zadeh LA (1965) Fuzzy sets. Inform Control 8(3):338–353zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PaderbornPaderbornGermany

Personalised recommendations