TSK-0 Fuzzy Rule-Based Systems for High-Dimensional Problems Using the Apriori Principle for Rule Generation

  • Javier Cózar
  • Luis de la Ossa
  • José A. Gámez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8536)


Algorithms which learn Linguistic Fuzzy Rule-Based Systems from data usually start up from the definition of the linguistic variables, generate a set of candidate rules and, afterwards, search a subset of them through a metaheuristic technique. In high-dimensional datasets the number of candidate rules is intractable, and a preselection is a must. This work adapts an existing preselection algorithm for Fuzzy Asociation Rule-Based Classification Systems to deal with TSK-0 LFRBSs. Experimental results show a good behaviour of the adaptation allowing to build precise and simple models for high-dimensional problems.


linguistic fuzzy modeling machine learning high- dimensional Takagi-Sugeno-Kang 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cordón, O., Herrera, F.: A proposal for improving the accuracy of linguistic modeling. IEEE Transactions on Fuzzy Systems 8(3), 335–344 (2000)CrossRefGoogle Scholar
  2. 2.
    Nozaki, K., Ishibuchi, H., Tanaka, H.: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems 86, 251–270 (1997)CrossRefGoogle Scholar
  3. 3.
    Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man, and Cybernetics 15(1), 116–132 (1985)Google Scholar
  4. 4.
    Alcala-Fdez, J., Alcala, R., Herrera, F.: A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Transactions on Fuzzy Systems 19(5), 857–872 (2011)CrossRefGoogle Scholar
  5. 5.
    Kavsek, B., Lavrac, N.: APRIORI-SD: Adapting association rule learning to subgroup discovery. Appl. Artif. Intell. 20(7), 543–583 (2006)CrossRefGoogle Scholar
  6. 6.
    Cózar, J., delaOssa, L., Gámez, J.A.: Learning TSK-0 linguistic fuzzy systems by means of Local Search Algorithms. Applied Soft Computing (2014), doi:10.1016/j.asoc.2014.03.003 (advance access published March 18, 2014)Google Scholar
  7. 7.
    Casillas, J., Cordon, O., Herrera, F.: COR: A methodology to improve ad hoc data-driven linguistic rule learning methods by including cooperation among rules. IEEE Transactions on Systems, Man and Cybernetics 32(4), 526–537 (2002)CrossRefGoogle Scholar
  8. 8.
    Nozaki, K., Ishibuchi, H., Tanaka, H.: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets and Systems 86, 251–270 (1997)CrossRefGoogle Scholar
  9. 9.
    Ishibuchi, H., Nakashima, T., Nii, M.: Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining. Springer, Berlin (2005)Google Scholar
  10. 10.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)CrossRefGoogle Scholar
  11. 11.
    Klema, V., Laub, A.: The singular value decomposition: Its computation and some applications. IEEE Transactions on Automatic Control 25(2), 164–176 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Cózar, J., de la Ossa, L., Gámez, J.: Using Apriori Algorithm + Standard Deviation to Improve the Scalability of TSK-0 Rule Learning Algorithms. In: XV Conferencia de la Asociación Española Para la Inteligencia Artificial (CAEPIA), pp. 99–108 (2013)Google Scholar
  13. 13.
    Zadeh, L.: The concept of a linguistic variable and its application to approximate reasoning. Information Science 8, 199–249 (1975)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Javier Cózar
    • 1
  • Luis de la Ossa
    • 1
  • José A. Gámez
    • 1
  1. 1.University of Castilla-La ManchaAlbaceteSpain

Personalised recommendations