Knowledge Supported Refinements for Rough Granular Computing: A Case of Life Insurance Industry

  • Kao-Yi ShenEmail author
  • Gwo-Hshiung Tzeng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9437)


Dominance-based rough set approach (DRSA) has been adopted in solving various multiple criteria classification problems with positive outcomes; its advantage in exploring imprecise and vague patterns is especially useful concerning the complexity of certain financial problems in business environment. Although DRSA may directly process the raw figures of data for classifications, the obtained decision rules (i.e., knowledge) would not be close to how domain experts comprehend those knowledge—composed of granules of concepts—without appropriate or suitable discretization of the attributes in practice. As a result, this study proposes a hybrid approach, composes of DRSA and a multiple attributes decision method, to search for suitable approximation spaces of attributes for gaining applicable knowledge for decision makers (DMs). To illustrate the proposed idea, a case of life insurance industry in Taiwan is analyzed with certain initial experiments. The result not only improves the classification accuracy of the DRSA model, but also contributes to the understanding of financial patterns in the life insurance industry.


Dominance-based rough set approach (DRSA) Multiple attribute decision making (MADM) DEMATEL-based ANP (DANP) Approximation space (AS) Granular computing Financial performance (FP) Life insurance industry 


  1. 1.
    Błaszczyński, J., Greco, S., Słowiński, R., Szelg, M.: Monotonic variable consistency rough set approaches. Int. J. Approx. Reason. 50(7), 979–999 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Cyran, K.A.: Quasi dominance rough set approach in testing for traces of natural selection at molecular level. In: Cyran, K.A., Kozielski, S., Peters, J.F., Stańczyk, U., Wakulicz-Deja, A. (eds.) Man-Machine Interactions. AISC, vol. 59, pp. 163–172. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Figueira, J., Greco, S., Ehrgott, M.: Multiple Criteria Decision Analysis: State of the Art Surveys, vol. 78. Springer Science & Business Media, New York (2005)CrossRefGoogle Scholar
  4. 4.
    Greco, S., Matarazzo, B., Słowiński, R., Stefanowski, J.: Variable consistency model of dominance-based rough sets approach. In: Ziarko, W.P., Yao, Y. (eds.) RSCTC 2000. LNCS (LNAI), vol. 2005, pp. 170–181. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  5. 5.
    Greco, S., Matarazzo, B., Słowiński, R.: Multicriteria classification by dominance-based rough set approach. In: Kloesgen, W., Zytkow, J. (eds.) Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)zbMATHGoogle Scholar
  6. 6.
    Liou, J.J.H., Tzeng, G.H.: Comments on “Multiple criteria decision making (MCDM) methods in economics: an overview”. Technol. Econ. Dev. Econ. 18(4), 672–695 (2012)CrossRefGoogle Scholar
  7. 7.
    OuYang, Y.P., Shieh, H.M., Tzeng, G.H.: A VIKOR technique based on DEMATEL and ANP for information security risk control assessment. Inf. Sci. 232, 482–500 (2013)CrossRefGoogle Scholar
  8. 8.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  9. 9.
    Peters, J.F.: Near sets. General theory about nearness of objects. Appl. Math. Sci. 1(53), 2609–2629 (2007)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Saaty, T.L.: Decision Making with Dependence and Feedback: The Analytic Network Process. RWS Publications, Pittsburgh (1996)Google Scholar
  11. 11.
    Shen, K.Y., Yan, M.R., Tzeng, G.H.: Combining VIKOR-DANP model for glamor stock selection and stock performance improvement. Knowl. -Based Syst. 58, 86–97 (2014)CrossRefGoogle Scholar
  12. 12.
    Shen, K.Y., Tzeng, G.H.: DRSA-based neuro-fuzzy inference system for the financial performance prediction of commercial bank. Int. J. Fuzzy Syst. 16(2), 173–183 (2014)Google Scholar
  13. 13.
    Shen, K.Y., Tzeng, G.H.: A decision rule-based soft computing model for supporting financial performance improvement of the banking industry. Soft. Comput. 19(4), 859–874 (2015)CrossRefGoogle Scholar
  14. 14.
    Shen, K.Y., Tzeng, G.H.: Combining DRSA decision-rules with FCA-based DANP evaluation for financial performance improvements. Technol. Econ. Dev. Econ. (2015, in press). doi: 10.3846/20294913.2015.1071295CrossRefGoogle Scholar
  15. 15.
    Shen, K.Y., Tzeng, G.H.: A new approach and insightful financial diagnoses for the IT Industry based on a hybrid MADM model. Knowl. -Based Syst. 85, 112–130 (2015)CrossRefGoogle Scholar
  16. 16.
    Shen, K.Y., Tzeng, G.H.: Fuzzy inference enhanced VC-DRSA model for technical analysis: investment decision aid. Int. J. Fuzzy Syst. 17(3), 375–389 (2015). doi: 10.1007/s40815-015-0058-8CrossRefGoogle Scholar
  17. 17.
    Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundam. Inform. 27, 245–253 (1996)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Skowron, A., Stepaniuk, J., Swiniarski, R.: Modeling rough granular computing based on approximation spaces. Inf. Sci. 184(1), 20–43 (2012)CrossRefGoogle Scholar
  19. 19.
    Taiwan Insurance Institute. Accessed 2014
  20. 20.
    Ziarko, W.: Variable precision rough sets model. J. Comput. Syst. Sci. 46(1), 39–59 (1993)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Zopounidis, C., Galariotis, E., Doumpos, M., Sarri, S., Andriosopoulos, K.: Multiple criteria decision aiding for finance: an updated bibliographic survey. Eur. J. Oper. Res. 247(2), 339–348 (2015). doi: 10.1016/j.ejor.2015.05.032MathSciNetCrossRefzbMATHGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Banking and FinanceChinese Culture University (SCE)TaipeiTaiwan
  2. 2.Graduate Institute of Urban Planning, College of Public AffairsNational Taipei UniversityNew Taipei CityTaiwan

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