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Learning efficient rulesets from fuzzy data with a genetic algorithm

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Foundations and Tools for Neural Modeling (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1606))

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Abstract

The aim of this paper is to demonstrate the feasibility of fuzzy measures of subsethood in learning from examples. Using the relationship between (fuzzy) set containment and (fuzzy) logical implication, a method of generating if-then rules that describe a fuzzy dataset is given. In order to obtain an efficient subset of the generated rules, we apply a simple genetic algorithm.

The proposed method is illustrated with a fuzzified well-known learning set. The results on this set clearly improve other approaches.

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References

  1. Botana, F., Bahamonde, A.: SHAPE: A machine learning system from examples. Int. J. Human-Comp. Studies 42 (1995) 137–155

    Article  Google Scholar 

  2. Botana, F.: Deriving fuzzy subsethood measures from violations of the implication between elements. Lec. Notes Artif. Intel. 1415 (1998) 234–243

    Google Scholar 

  3. Botana, F.: Learning rules from fuzzy datasets. Proc. 5th Europ. Congress on Intel. Techniques and Soft Comp., Aachen, Germany (1997) 1109–1113

    Google Scholar 

  4. Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  5. Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, H.: Construction of fuzzy classification systems with rectangular fuzzy rules using genetic algorithms. Fuzzy Sets and Systems 65 (1994) 237–253

    Article  MathSciNet  Google Scholar 

  6. Janikow, C.Z.: Fuzzy decision trees: Issues and methods. IEEE Trans. Systems Man Cybernet. Part B 28 1 (1998) 1–14

    Article  Google Scholar 

  7. Kosko, B.: Neural networks and fuzzy systems. Prentice Hall, Englewood Cliffs (1992)

    MATH  Google Scholar 

  8. Lee, C.C.: Fuzzy logic in control systems, Fuzzy logic controller-Parts I and II. IEEE Trans. Systems, Man Cybernet. 20 (1990) 404–435

    Article  MathSciNet  MATH  Google Scholar 

  9. Lim, M.H., Rahardja, S., Gwee, B.H.: A GA paradigm for learning fuzzy rules. Fuzzy Sets and Systems 82 (1996) 177–186

    Article  MathSciNet  Google Scholar 

  10. Michalski, R.S., Chilausky, R.L.: Learning by being told and learning from examples. Int. J. Policy Anal. Inform. Systems 4 2 (1980) 125–160

    Google Scholar 

  11. Michalski, R.S., Mozetic, I., Hong, J., Lavrac, N.: Multi-purpose incremental learning system AQ15 and its testing application to three medical domains. Proc. AAAI-86, 1041–1045. Morgan Kaufmann, Philadelphia (1986)

    Google Scholar 

  12. Michalski, R.S., Kaufman, K.: Learning from inconsistent or noisy data: the ENIL/AQ18 approach. Lec. Notes Artif. Intel. (1999), to appear

    Google Scholar 

  13. Pappis, C.P., Karacapilidis, N.I.: A comparative assessment of measures of similarity of fuzzy values. Fuzzy Sets and Systems 56 (1993) 171–174

    Article  MathSciNet  MATH  Google Scholar 

  14. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)

    Google Scholar 

  15. Quinlan, J.R.: Induction of decision trees. Mach. Learning 1(1) (1986) 81–106

    Google Scholar 

  16. Sánchez, E.: Inverses of fuzzy relations. Application to possibility distributions and medical diagnosis. Fuzzy Sets and Systems 2 (1979) 75–86

    Article  MathSciNet  MATH  Google Scholar 

  17. Sugeno, M.: An introductory survey of fuzzy control. Inform. Sci. 36 (1985) 59–83

    Article  MathSciNet  MATH  Google Scholar 

  18. Wang, L.X., Mendel, J.M.: Generating fuzzy rules by learning from examples. IEEE Trans. Systems Man Cybernet. 22 6 (1992) 1414–1427

    Article  MathSciNet  Google Scholar 

  19. Weber, R.: Fuzzy-ID3: a class of methods for automatic knowledge acquisition. Proc. 2nd Int. Conf. on Fuzzy Logic,Iizuka, Japan, (1992) 265–268

    Google Scholar 

  20. Young, V. R.: Fuzzy subsethood. Fuzzy Sets and Systems 77 (1996) 371–384

    Article  MathSciNet  MATH  Google Scholar 

  21. Yuan, Y., Shaw, M. J.: Induction of fuzzy decision trees. Fuzzy Sets and Systems 69 (1995) 125–139

    Article  MathSciNet  Google Scholar 

  22. Zadeh, L.A.: Fuzzy sets. Inform. Control 8 (1965) 338–353

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Departamento de Matemática Aplicada, Universidad de Vigo, Campus A Xunqueira, 36005, Pontevedra, Spain

    Francisco Botana

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  1. Francisco Botana
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José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Botana, F. (1999). Learning efficient rulesets from fuzzy data with a genetic algorithm. In: Mira, J., Sánchez-Andrés, J.V. (eds) Foundations and Tools for Neural Modeling. IWANN 1999. Lecture Notes in Computer Science, vol 1606. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0098209

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  • DOI: https://doi.org/10.1007/BFb0098209

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66069-9

  • Online ISBN: 978-3-540-48771-5

  • eBook Packages: Springer Book Archive

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