Using DRSA and Fuzzy Measure to Enlighten Policy Making for Enhancing National Competitiveness by WCY 2011

  • Yu-Chien Ko
  • Hamido Fujita
  • Gwo-Hshiung Tzeng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7345)


The fuzzy measure of competitiveness criteria can be used to enlighten policy making for enhancing national competitiveness. However, fuzzy densities and interactions among criteria are usually unknown or uncertain for implications thus making analysis complicated and hard. This research proposes an extended fuzzy measure to non-additively (or called super-additively) aggregate preferences and implication possibilities into utilities or values, and then implies competitiveness features, patterns, and trends based on the utilities or values. Technically, the dominance-based rough set approach (DRSA) is used to transform ‘if…then...’ implications into fuzzy densities. For illustration, the extended fuzzy measure is applied on World Competitiveness Yearbook 2011 for analyzing Greece, Italy, Portugal, and Spain, then how making policy for avoiding debt crisis and enhancing national competitiveness.


fuzzy measure national competitiveness dominance-based rough set approach (DRSA) World Competitiveness Yearbook (WCY) 


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  1. 1.
    Grabisch, M.: The representation of importance and interaction of features by fuzzy measures. Pattern Recognition Letters 17(6), 567–575 (1996)CrossRefGoogle Scholar
  2. 2.
    Mikenina, L., Zimmermann, H.-J.: Improved feature selection and classification by the 2-additive fuzzy measure. Fuzzy Sets and Systems 107(2), 197–218 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Grabisch, M., Sugeno, M.: Multi-attribute classification using fuzzy integral. In: IEEE International Conference in Fuzzy Systems 1992, pp. 47–54 (1992)Google Scholar
  4. 4.
    Keller, J.M., Gader, P., Tahani, H., Chiang, J.-H., Mohamed, M.: Advances in fuzzy integration for pattern recognition. Fuzzy Sets and Systems 65(2-3), 273–283 (1994)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Grabisch, M.: Fuzzy integral in multicriteria decision making. Fuzzy Sets and Systems 69(3), 279–298 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Grabisch, M.: The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research 89(3), 445–456 (1996)zbMATHCrossRefGoogle Scholar
  7. 7.
    Yang, J.L., Chiu, H.N., Tzeng, G.H.: Vendor selection by integrated fuzzy MCDM techniques with independent and interdependent relationships. Information Sciences 178(21), 4166–4183 (2008)zbMATHCrossRefGoogle Scholar
  8. 8.
    Larbani, M., Huang, C.Y., Tzeng, G.H.: A novel method for fuzzy measure identification. International Journal of Fuzzy Systems 13(1), 24–34 (2011)MathSciNetGoogle Scholar
  9. 9.
    Sugeno, M.: Theory of fuzzy integrals and its applications. Ph.D. thesis, Tokyo Institute of Technology, Tokyo, Japan (1974)Google Scholar
  10. 10.
    Tahani, H., Keller, J.: Information Fusion in Computer Vision Using the Fuzzy Integral. IEEE Transactions on Systems, Man, Cybernetics 20(3), 733–741 (1990)CrossRefGoogle Scholar
  11. 11.
    Lee, K.-Y., Leekwang, H.: Identification of λ-fuzzy measure by genetic algorithms. Fuzzy Sets and Systems 75(3), 301–309 (1995)MathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Larbani, M., Huang, C.Y., Tzeng, G.H.: A Novel Method for Fuzzy Measure Identification. International Journal of Fuzzy Systems 13(1), 24–34 (2011)MathSciNetGoogle Scholar
  13. 13.
    Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation of a preference relation by dominance relations. European Journal of Operational Research 117(1), 63–83 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Greco, S., Matarazzo, B., Slowinski, R.: Extension of the rough set approach to multicriteria decision support. INFOR 38(3), 161–193 (2000)Google Scholar
  15. 15.
    Greco, S., Matarazzo, B., Slowinski, R.: Rough set theory for multicriteria decision analysis. European Journal of Operational Research 129(1), 1–47 (2001)MathSciNetzbMATHCrossRefGoogle Scholar
  16. 16.
    Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation by dominance relations. International Journal of Intelligent Systems 17(2), 153–171 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  17. 17.
    Fan, T.F., Liu, D.R., Tzeng, G.H.: Rough set-based logics for multicriteria decision analysis. European Journal of Operational Research 82(1), 340–355 (2007)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Liou, J.J.H., Tzeng, G.H.: A dominance-based rough set approach to customer behavior in the airline market. Information Sciences 180(11), 2230–2238 (2010)CrossRefGoogle Scholar
  19. 19.
    Shyng, J.Y., Shieh, H.M., Tzeng, G.H.: Compactness rate as a rule selection index based on rough set theory to improve data analysis for personal investment portfolios. Applied Soft Computing 11(4), 3671–3679 (2011)CrossRefGoogle Scholar
  20. 20.
    Pawlak, Z.: Rough set approach to knowledge-based decision support. European Journal of Operational Research 99, 48–57 (1997)zbMATHCrossRefGoogle Scholar
  21. 21.
    Pawlak, Z.: Rough set, decision algorithm, and Bayes’ theorem. European Journal of Operational Research 136(1), 181–189 (2002)MathSciNetzbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yu-Chien Ko
    • 1
  • Hamido Fujita
    • 2
  • Gwo-Hshiung Tzeng
    • 3
    • 4
  1. 1.Department of Information ManagementChung Hua UniversityHsinchuTaiwan
  2. 2.Software and Information ScienceIwate Prefectural UniversityTakizawaJapan
  3. 3.Graduate Institute of Project ManagementKainan UniversityTaoyuanTaiwan
  4. 4.Institute of Management of TechnologyNational Chiao Tung UniversityHsinchuTaiwan

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