Soft Computing

, Volume 13, Issue 5, pp 511–519 | Cite as

Parallel distributed genetic fuzzy rule selection

  • Yusuke Nojima
  • Hisao Ishibuchi
  • Isao Kuwajima


Genetic fuzzy rule selection has been successfully used to design accurate and compact fuzzy rule-based classifiers. It is, however, very difficult to handle large data sets due to the increase in computational costs. This paper proposes a simple but effective idea to improve the scalability of genetic fuzzy rule selection to large data sets. Our idea is based on its parallel distributed implementation. Both a training data set and a population are divided into subgroups (i.e., into training data subsets and sub-populations, respectively) for the use of multiple processors. We compare seven variants of the parallel distributed implementation with the original non-parallel algorithm through computational experiments on some benchmark data sets.


Genetic fuzzy rule selection Parallel distributed implementation Data subdivision Fuzzy rule-based classifier 


  1. Abraham A, Jain L, Goldberg R (eds) (2005) Evolutionary multiobjective optimization. Springer, LondonzbMATHGoogle Scholar
  2. Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo AI (1996) Fast discovery of association rules. In: Fayyad UM et al (eds) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, pp 307–328Google Scholar
  3. Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6(5):443–462CrossRefGoogle Scholar
  4. Araujo DLA, Lopes HS, Freitas AA (2000) Rule discovery with a parallel genetic algorithm. In: Proceedings of GECCO Workshop on Data Mining with Evolutionary Computation, pp 89–92Google Scholar
  5. Cano JR, Herrera F, Lozano M (2005) Stratification for scaling up evolutionary prototype selection. Pattern Recognit Lett 26(7):953–963CrossRefGoogle Scholar
  6. Cano JR, Herrera F, Lozano M (2006) On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. Appl Soft Comput 6(3):323–332CrossRefGoogle Scholar
  7. Cantu-Paz E (1997) A survey of parallel genetic algorithms, IlliGAL Report No. 95003Google Scholar
  8. Casillas J, Cordon O, Herrera F, Magdalena L (eds) (2003a) Interpretability issues in fuzzy modeling. Springer, BerlinzbMATHGoogle Scholar
  9. Casillas J, Cordon O, Herrera F, Magdalena L (eds) (2003b) Accuracy improvements in linguistic fuzzy modeling. Springer, BerlinzbMATHGoogle Scholar
  10. Coello CAC (1999) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl Inform Syst 1(3):269–308Google Scholar
  11. Coello CAC, van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems. Kluwer, BostonzbMATHGoogle Scholar
  12. Cordon O, del Jesus MJ, Herrera F (1999) A proposal on reasoning methods in fuzzy rule-based classification systems. Int J Approx Reason 20(1):21–45Google Scholar
  13. Cordon O, Herrera F, Hoffman F, Magdalena L (2001) Genetic fuzzy systems. World Scientific, SingaporeGoogle Scholar
  14. Cordon O, Gomide F, Herrera F, Hoffmann F, Magdalena L (2004) Ten years of genetic fuzzy systems: current framework and new trends. Fuzzy Sets Syst 141(1):5–31zbMATHCrossRefMathSciNetGoogle Scholar
  15. Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterzbMATHGoogle Scholar
  16. Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, BerlinzbMATHGoogle Scholar
  17. Herrera F (2005) Genetic fuzzy systems: Status, critical considerations and future directions. Int J Comput Intell Res 1(1):59–67Google Scholar
  18. Ishibuchi H. Nojima Y, Kuwajima I (2006) Genetic rule selection as a postprocessing procedure in fuzzy data mining. In: Proceedings of 2006 International Symposium on Evolving Fuzzy Systems, pp 286–291Google Scholar
  19. Ishibuchi H (2007) Evolutionary multiobjective design of fuzzy rule-based systems. In: Proceedings of First IEEE Symposium on Foundations of Computational Intelligence, pp 9–16Google Scholar
  20. Ishibuchi H, Nakashima T (2001) Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 9(4):506–515CrossRefGoogle Scholar
  21. Ishibuchi H, Yamamoto T (2004) Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining. Fuzzy Sets Syst 141(1):59–88zbMATHCrossRefMathSciNetGoogle Scholar
  22. Ishibuchi H, Nozaki K, Tanaka H (1992) Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst 52(1):21–32CrossRefGoogle Scholar
  23. Ishibuchi H, Nozaki K, Yamamoto N, Tanaka H (1995) Selecting fuzzy if-then rules for classification problems using genetic algorithms. IEEE Trans Fuzzy Syst 3(3):260–270CrossRefGoogle Scholar
  24. Ishibuchi H, Murata T, Turksen IB (1997) Single-objective and two-objective genetic algorithms for selecting linguistic rules for pattern classification problems. Fuzzy Sets Syst 89(2):135–150CrossRefGoogle Scholar
  25. Ishibuchi H, Nakashima T, Morisawa T (1999) Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets Syst 103(2):223–238CrossRefGoogle Scholar
  26. Ishibuchi H, Nakashima T, Murata T (2001) Three-objective genetics-based machine learning for linguistic rule extraction. Inform Sci 136(1–4):109–133zbMATHCrossRefGoogle Scholar
  27. Ishibuchi H, Nakashima T, Nii M (2004) Classification and modeling with linguistic information granules: advanced approaches to linguistic data mining. Springer, BerlinGoogle Scholar
  28. Ishibuchi H, Kuwajima I, Nojima Y (2007) Use of Pareto-optimal and near Pareto-optimal rules as candidate rules in genetic fuzzy rule selection. In: Melin P et al (eds) Analysis and design of intelligent systems using soft computing techniques (Advances in Soft Computing 41). Springer, Berlin, pp 387–396CrossRefGoogle Scholar
  29. Jin Y (ed) (2006) Multi-objective machine learning. Springer, BerlinzbMATHGoogle Scholar
  30. Liu H, Motoda H (1998a) Feature selection for knowledge discovery and data mining. Kluwer, DordrechtGoogle Scholar
  31. Liu H, Motoda H (1998b) Instance selection and construction for data mining. Kluwer, DordrechtGoogle Scholar
  32. Llora X, Garrell JM (2001) Knowledge-independent data mining with fine-grained parallel evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp 461–468Google Scholar
  33. Llora X, Garrell JM (2002) Coevolving different knowledge representations with fine-grained parallel learning classifier systems. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp 934–941Google Scholar
  34. Nojima Y, Ishibuchi H (2008) Computational efficiency of parallel distributed genetic fuzzy rule selection for large data sets. In: Proceedings of Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp 1137–1142Google Scholar
  35. Nojima Y, Ishibuchi H, Kuwajima I (2006) Comparison of search ability between genetic fuzzy rule selection and fuzzy genetics-based machine learning In: Proceedings of 2006 International Symposium on Evolving Fuzzy Systems, pp 125–130Google Scholar
  36. Sheskin D (2007) Handbook of parametric and nonparametric statistical procedures, 4th edn. Chapman & Hall, LondonGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  1. 1.Graduate School of EngineeringOsaka Prefecture UniversitySakaiJapan

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