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01.03.2009 | Focus

Parallel distributed genetic fuzzy rule selection

verfasst von: Yusuke Nojima, Hisao Ishibuchi, Isao Kuwajima

Erschienen in: Soft Computing | Ausgabe 5/2009

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Abstract

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.

Vorheriger Artikel Processing time estimations by variable structure TSK rules learned through genetic programming

Nächster Artikel A genetic-fuzzy mining approach for items with multiple minimum supports

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Abraham A, Jain L, Goldberg R (eds) (2005) Evolutionary multiobjective optimization. Springer, LondonMATH

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 307–328

Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6(5):443–462CrossRef

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 89–92

Cano JR, Herrera F, Lozano M (2005) Stratification for scaling up evolutionary prototype selection. Pattern Recognit Lett 26(7):953–963CrossRef

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):323–332CrossRef

Cantu-Paz E (1997) A survey of parallel genetic algorithms, IlliGAL Report No. 95003

Casillas J, Cordon O, Herrera F, Magdalena L (eds) (2003a) Interpretability issues in fuzzy modeling. Springer, BerlinMATH

Casillas J, Cordon O, Herrera F, Magdalena L (eds) (2003b) Accuracy improvements in linguistic fuzzy modeling. Springer, BerlinMATH

Coello CAC (1999) A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl Inform Syst 1(3):269–308

Coello CAC, van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems. Kluwer, BostonMATH

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):21–45

Cordon O, Herrera F, Hoffman F, Magdalena L (2001) Genetic fuzzy systems. World Scientific, Singapore

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):5–31MATHCrossRefMathSciNet

Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterMATH

Freitas AA (2002) Data mining and knowledge discovery with evolutionary algorithms. Springer, BerlinMATH

Herrera F (2005) Genetic fuzzy systems: Status, critical considerations and future directions. Int J Comput Intell Res 1(1):59–67

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 286–291

Ishibuchi H (2007) Evolutionary multiobjective design of fuzzy rule-based systems. In: Proceedings of First IEEE Symposium on Foundations of Computational Intelligence, pp 9–16

Ishibuchi H, Nakashima T (2001) Effect of rule weights in fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 9(4):506–515CrossRef

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):59–88MATHCrossRefMathSciNet

Ishibuchi H, Nozaki K, Tanaka H (1992) Distributed representation of fuzzy rules and its application to pattern classification. Fuzzy Sets Syst 52(1):21–32CrossRef

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):260–270CrossRef

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):135–150CrossRef

Ishibuchi H, Nakashima T, Morisawa T (1999) Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets Syst 103(2):223–238CrossRef

Ishibuchi H, Nakashima T, Murata T (2001) Three-objective genetics-based machine learning for linguistic rule extraction. Inform Sci 136(1–4):109–133MATHCrossRef

Ishibuchi H, Nakashima T, Nii M (2004) Classification and modeling with linguistic information granules: advanced approaches to linguistic data mining. Springer, Berlin

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 387–396CrossRef

Jin Y (ed) (2006) Multi-objective machine learning. Springer, BerlinMATH

Liu H, Motoda H (1998a) Feature selection for knowledge discovery and data mining. Kluwer, Dordrecht

Liu H, Motoda H (1998b) Instance selection and construction for data mining. Kluwer, Dordrecht

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 461–468

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 934–941

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 1137–1142

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 125–130

Sheskin D (2007) Handbook of parametric and nonparametric statistical procedures, 4th edn. Chapman & Hall, London

Titel: Parallel distributed genetic fuzzy rule selection
verfasst von: Yusuke Nojima
Hisao Ishibuchi
Isao Kuwajima
Publikationsdatum: 01.03.2009
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 5/2009
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-008-0365-1

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