Construction of Efficient Rulesets from Fuzzy Data through Simulated Annealing

  • Francisco Botana
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1904)


This paper proposes a simulated annealing-based approach for obtaining compact efficient classification systems from fuzzy data. Different methods for generating decision rules from fuzzy data share a problem in multidimensional spaces: their high cardinality. In order to solve it, the method of simulated annealing is proposed. This approach is illustrated with two well-known learning sets.


Simulated Annealing Fuzzy Rule Fuzzy Data Weight Lift High Membership 
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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  1. 1.
    Blake, C., Keogh, E., Merz, C. J. UCI Repository of machine learning databases []. Irvine, CA: University of California
  2. 2.
    Botana, F.: Learning rules from fuzzy datasets. Proc. 5th Europ. Congress on Intel. Techniques and Soft Comp., Aachen, Germany (1997) 1109–1113Google Scholar
  3. 3.
    Botana, F.: Deriving fuzzy subsethood measures from violations of the implication between elements. Lec. Notes Artif. Intel. 1415 (1998) 234–243Google Scholar
  4. 4.
    Černy, V.: Thermodynamical Approach to the Traveling Salesman Problem: An Efficient Simulation Algorithm. J. Optim. Theory Appl. 45 (1985) 41–51CrossRefMathSciNetzbMATHGoogle Scholar
  5. 5.
    Hirsh, H.: Generalizing version spaces. Mach. Learning 17 (1994) 5–46zbMATHGoogle Scholar
  6. 6.
    Hong, T. P., Tseng, S.S.: A generalized version space learning algorithm for noisy and uncertain data. IEEE Trans. Knowledge Data Eng 9 (1997) 336–340CrossRefGoogle Scholar
  7. 7.
    Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms. Fuzzy Sets Syst. 65 (1994) 237–253CrossRefMathSciNetGoogle Scholar
  8. 8.
    Kirpatrick, S., Gelatt, C.D., Vecchi, M. P.: Optimization by Simulated Annealing. Science 220 (1983) 671–680CrossRefMathSciNetGoogle Scholar
  9. 9.
    Kosko, B.: Neural networks and fuzzy systems. Prentice Hall, Englewood Cliffs (1992)zbMATHGoogle Scholar
  10. 10.
    Laarhoven, P., Aarts, E.: Simulated Annealing: Theory and Applications. Reidel, Dordrecht (1987)zbMATHGoogle Scholar
  11. 11.
    Quinlan, J.R.: Induction of decision trees. Mach. Learning 1(1) (1986) 81–106Google Scholar
  12. 12.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  13. 13.
    Sugeno, M.: An Introductory Survey of Fuzzy Control. Inf. Sci. 36 (1985) 59–83zbMATHMathSciNetGoogle Scholar
  14. 14.
    Takagi, T., Sugeno, M.: Fuzzy Identification of Systems and its Applications to Modeling and Control. IEEE Trans. Systems Man Cybernet. 15 (1985) 116–132zbMATHGoogle Scholar
  15. 15.
    Wang, L. X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Systems Man Cybernet. 226(1992) 1414–1427CrossRefMathSciNetGoogle Scholar
  16. 16.
    Wang, C. H., Liu, J.F. et al.: A fuzzy inductive learning strategy for modular rules. Fuzzy Sets Syst. 103 (1999) 91–105CrossRefGoogle Scholar
  17. 17.
    Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets Syst. 69 (1995) 125–139CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Francisco Botana
    • 1
  1. 1.Departamento de Matemática AplicadaUniversidad de VigoPontevedraSpain

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