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Decision-Oriented Rough Set Methods

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

Abstract

Rough set theory is a very effective multi-attribute decision analysis tool. The paper reviews four decision-oriented rough set models and methods: dominance-based rough set, three-way decisions, multigranulation decision-theoretic rough set and rough set based multi-attribute group decision-making model. We also introduce some of our group’s works under these four models. Several future research directions of decision-oriented rough sets are presented in the end of the paper.

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References

  1. Zopounidis, C., Doumpos, M.: Multicriteria classification and sorting methods: a literature review. Eur. J. Oper. Res. 138, 229–246 (2002)

    MATH  Google Scholar 

  2. Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making-Methods and Applications: A State of the Art Survey. Lecture Notes in Economics and Mathematical Systems. Springer-Verlag, New York (1981)

    MATH  Google Scholar 

  3. Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, Now York (1980)

    MATH  Google Scholar 

  4. Benayoun, R., Roy, B., Sussman, N.: Manual de refrence du programme electre. Note de Synthese et Formation, No. 25. Paris: Direction Scientifique SEMA (1966)

    Google Scholar 

  5. Brans, J.P., Mareschal, B.: The promethee vi procedure: how to differentiate hard from soft multicriteria problems. J. Decis. Syst. 4, 213–223 (1995)

    Google Scholar 

  6. Hwang, C.L., Lai, Y.J., Liu, T.Y.: A new approach for multiple objective decision making. Comput. Oper. Res. 20, 889–899 (1993)

    MATH  Google Scholar 

  7. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    MATH  Google Scholar 

  8. Pawkak, Z.: Rough set approach to knowledge-based decision support. Eur. J. Oper. Res. 99, 48–57 (1997)

    Google Scholar 

  9. Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)

    MATH  Google Scholar 

  10. Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation of a preference relation by dominance relations. Eur. J. Oper. Res. 117, 63–83 (1999)

    MATH  Google Scholar 

  11. Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. In: Slowinski, R. (eds.) Intelligent Decision Support - Handbook of Applications and Advances of the Rough Sets Theory, vol. 11, pp. 331–362. Springer (1991)

    Google Scholar 

  12. Wang, G.Y., Yu, H., Yang, D.C.: Decision table reduction based on conditional information entropy. Chin. J. Comput. 25, 759–766 (2002)

    MathSciNet  Google Scholar 

  13. Liang, J.Y., Wang, F., Dang, C.Y., Qian, Y.H.: An efficient rough feature selection algorithm with a multi-granulation view. Int. J. Approx. Reason. 53, 912–926 (2010)

    MathSciNet  Google Scholar 

  14. Liang, J.Y., Wang, F., Dang, C.Y., Qian, Y.H.: A group incremental approach to feature selection applying rough set technique. IEEE Trans. Knowl. Data Eng. 26, 294–308 (2014)

    Google Scholar 

  15. Slezak, D.: Approximate entropy reducts. Fund. Inform. 53, 365–390 (2002)

    MathSciNet  MATH  Google Scholar 

  16. Grzymala-Busse, J.W.: LERS: A system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set theory, vol. 11, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  17. Greco, S., Matarazzo, B., Slowinski, R.: The use of rough sets and fuzzy sets in MCDM. In: Gal, T., Hanne, T., Stewart, T. (eds.) Advances in Multiple Criteria decision Making. Kluwer Academic Publishers, Dordrecht (1999)

    MATH  Google Scholar 

  18. Grzymala-Busse, J.W., Stefanowski, J.: Three discretization methods for rule induction. Int. J. Intell. Syst. 26, 29–38 (2001)

    MATH  Google Scholar 

  19. Leung, Y., Fischer, M.M., Wu, W.Z., Mi, J.S.: A rough set approach for the discovery of classification rules in interval-valued information systems. Int. J. Approx. Reason. 47, 233–246 (2008)

    MathSciNet  MATH  Google Scholar 

  20. Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17, 191–209 (1990)

    MATH  Google Scholar 

  21. Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, vol. 11, pp. 203–232. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  22. Hu, Q.H., Xie, Z.X., Yu, D.R.: Hybrid attribute reduction based on a novel fuzzy rough model and information granulation. Pattern Recogn. 40, 3509–3521 (2007)

    MATH  Google Scholar 

  23. Greco, S., Matarazzo, B., Slowinski, R.: Rough sets theory for multicriteria decision analysis. Eur. J. Oper. Res. 129, 1–7 (2001)

    MATH  Google Scholar 

  24. Greco, S., Matarazzo, B., Slowinski, R.: Rough sets methodology for sorting problems in presence of multiple attributes and criteria. Eur. J. Oper. Res. 138, 247–259 (2002)

    MathSciNet  MATH  Google Scholar 

  25. Greco, S., Matarazzo, B., Slowinski, R., Zanakis, S.: Global investing risk: a case study of knowledge assessment via rough sets. Annal Oper. Res. 185, 105–138 (2011)

    MathSciNet  Google Scholar 

  26. Greco, S., Slowinski, R., Zielniewicz, P.: Putting dominance-based rough set approach and robust ordinal regression together. Dec. Support Syst. 54, 891–903 (2013)

    Google Scholar 

  27. Wong, S.K.M., Ziarko, W.: Comparison of the probabilistic approximate classification and the fuzzy set model. Fuzzy Sets Syst. 21, 357–362 (1987)

    MathSciNet  MATH  Google Scholar 

  28. Pawlak, Z., Wong, S.K.M., Ziarko, W.: Rough sets: probabilistic versus deterministic approach. Int. J. Man-Mach. Stud. 29, 81–95 (1988)

    MATH  Google Scholar 

  29. Ziarko, W.: Variable precision rough set model. J. Comput. Syst. Sci. 46, 39–59 (1993)

    MathSciNet  MATH  Google Scholar 

  30. Yao, Y.Y., Wong, S.K.M.: A decisoin theoretic framework for approximating concepts. Int. J. Man-Mach. Stud. 37, 793–809 (1992)

    Google Scholar 

  31. Yao, Y.Y., Zhou, B.: Naive Bayesian Rough Sets. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds.) RSKT 2010. LNCS (LNAI), vol. 6401, pp. 719–726. Springer, Heidelberg (2010)

    Google Scholar 

  32. Zhu, W., Wang, F.Y.: Reduction and axiomization of covering generalized rough sets. Inf. Sci. 152, 217–230 (2003)

    MathSciNet  MATH  Google Scholar 

  33. Qian, Y.H., Liang, J.Y., Yao, Y.Y., Dang, C.Y.: MGRS: A multi-granulation rough set. Inf. Sci. 180, 949–970 (2010)

    MathSciNet  MATH  Google Scholar 

  34. Yang, X.B., Song, X.N., Chen, Z.H., Yang, J.Y.: On multi-granulation rough sets in incomplete information system. Int. J. Mach. Learn. Cyber. 3, 223–232 (2011)

    Google Scholar 

  35. Xu, W.H., Sun, W.X., Zhang, X.Y., Zhang, W.X.: Multiple granulation rough set approach to ordered information systems. Inter. J. General Syst. 41, 475–501 (2012)

    MathSciNet  MATH  Google Scholar 

  36. Lin, G.P., Liang, J.Y., Qian, Y.H.: Multigranulation rough sets: from partition to covering. Inf. Sci. 241, 101–118 (2013)

    MathSciNet  MATH  Google Scholar 

  37. Liou, J.J.H., Tzeng, G.H.: A dominance-based rough set approach to customer behavior in the airline market. Inf. Sci. 180, 2230–2238 (2010)

    Google Scholar 

  38. Hu, Q.H., Yu, D.R., Guo, M.Z.: Fuzzy preference based rough sets. Inf. Sci. 180, 2003–2022 (2010)

    MathSciNet  MATH  Google Scholar 

  39. Szelag, M., Greco, S., Slowinski, R.: Variable consistency dominance-based rough set approach to preference learning in multicriteria ranking. Inf. Sci. 277, 525–552 (2014)

    MathSciNet  MATH  Google Scholar 

  40. Song, P., Liang, J.Y., Qian, Y.H.: A two-grade approach to ranking interval data. Knowl.-Based Syst. 27, 234–244 (2012)

    Google Scholar 

  41. Yao, Y.Y.: An Outline of a Theory of Three-Way Decisions. In: Yao, J., Yang, Y., Słowiński, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) RSCTC 2012. LNCS (LNAI), vol. 7413, pp. 1–17. Springer, Heidelberg (2012)

    Google Scholar 

  42. Yao, Y.Y., Wong, S.K.M., Lingras, P.: A decision-theoretic rough set model. In: Ras, Z.W., Zemankova, M., Emrich, M.L. (eds.) Methodologies for Intelligent Systems, vol. 5, pp. 17–25. North-Holland, New York (1990)

    Google Scholar 

  43. Greco, S., Słowiński, R., Yao, Y.Y.: Bayesian Decision Theory for Dominance-Based Rough Set Approach. In: Yao, J.T., Lingras, P., Wu, W.-Z., Szczuka, M.S., Cercone, N.J., Ślȩzak, D. (eds.) RSKT 2007. LNCS (LNAI), vol. 4481, pp. 134–141. Springer, Heidelberg (2007)

    Google Scholar 

  44. Herbert, J.P., Yao, J.T.: Game-Theoretic Risk Analysis in Decision-Theoretic Rough Sets. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 132–139. Springer, Heidelberg (2008)

    Google Scholar 

  45. Liang, D.C., Liu, D.: Deriving three-way decisions from intuitionistic fuzzy decision theoretic rough sets. Inf. Sci. 200, 28–48 (2015)

    MathSciNet  MATH  Google Scholar 

  46. Yao, Y.Y.: Granular Computing and Sequential Three-Way Decisions. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) RSKT 2013. LNCS (LNAI), vol. 8171, pp. 16–27. Springer, Heidelberg (2013)

    Google Scholar 

  47. Wang, B.L., Liang, J.Y.: A Novel Intelligent Multi-attribute Three-Way Group Sorting Method Based on Dempster-Shafer Theory. In: Miao, D.Q., Pedrycz, W., Slezak, D., Peters, G., Hu, Q., Wang, R. (eds.) RSKT 2014. LNCS (LNAI), vol. 8818, pp. 789–800. Springer, Heidelberg (2014)

    Google Scholar 

  48. Qian, Y.H., Zhang, H., Sang, Y.L., Liang, J.Y.: Multi-granulation decision-theoretic rough sets. Int. J. Approx. Reason. 55, 225–237 (2014)

    MATH  Google Scholar 

  49. Liang, J.Y., Wang, B.L.: Rough set based multi-attribute group decision making model. In: Jia, X.Y., Shang, L., Zhou X. Z. et al. Three-way Decision Theory and Applications, pp. 131–148. Nanjing University Press, Nanjing (2012)

    Google Scholar 

  50. Pang, J.F., Liang, J.Y.: Evaluation of the results of multi-attribute group decision-making with linguistic information. OMEGA 40, 294–301 (2012)

    Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 61432011, U1435212), Research Project Supported by Shanxi Scholarship Council of China (No. 2013-101), the Key Problems in Science and Technology Project of Shanxi Province (No. 20110321027-01) and the Construction Project of the Science and Technology Basic Condition Platform of Shanxi Province (No. 2012091002-0101).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Taiyuan Normal University, Jinzhong, 030619, Shanxi, China

    Jiye Liang

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, Shanxi, China

    Jiye Liang

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  1. Jiye Liang
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Correspondence to Jiye Liang .

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Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

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Liang, J. (2015). Decision-Oriented Rough Set Methods. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

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