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A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

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Computational Collective Intelligence

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9330))

Abstract

Rough set theory (RST) is one of the data mining tools, which have many capabilities such as to minimize the size of an input data and to produce sets of decision rules from a set of data. RST is also one of the great techniques used in dealing with ambiguity and uncertainty of datasets. It was introduced by Z. Pawlak in 1997 and until now, there are many researchers who really make use of its advantages either to make an enhancement of the RST or to apply in various research areas such as in decision analysis, pattern recognition, machine learning, intelligent systems, inductive reasoning, data preprocessing, knowledge discovery, and expert systems. This paper presents a recent study on the elementary concepts of RST and its implementation in the multi-criteria decision analysis (MCDA) problems.

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References

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

    Article  MATH  Google Scholar 

  2. Mahajan, P., Kandwal, R., Vijay, R.: Rough Set Approach in Machine Learning: A Review. Int. J. Comput. Appl. 56(10), 1–13 (2012)

    Google Scholar 

  3. Li, R., Wang, Z.: Mining classification rules using rough sets and neural networks. Eur. J. Oper. Res. 157, 439–448 (2004)

    Article  MATH  Google Scholar 

  4. Lin, G., Liang, J., Qian, Y.: Multigranulation rough sets: From partition to covering. Inf. Sci. (Ny) 241, 101–118 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  5. Nguyen, H.S., Skowron, A.: Rough Sets: From Rudiments to Challenges. In: Intell. Syst. Ref. Libr., vol. 42, pp. 75–173 (2013)

    Google Scholar 

  6. Fan, T.-F., Liau, C.-J., Liu, D.-R.: Dominance-based fuzzy rough set analysis of uncertain and possibilistic data tables. Int. J. Approx. Reason. 52(9), 1283–1297 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  7. Liao, S.-H., Chen, Y.-J.: A rough set-based association rule approach implemented on exploring beverages product spectrum. Appl. Intell. 40, 464–478 (2013)

    Article  Google Scholar 

  8. Wang, C.H., Chin, Y.C., Tzeng, G.H.: Mining the R&D innovation performance processes for high-tech firms based on rough set theory. Technovation 30(7–8), 447–458 (2010)

    Article  Google Scholar 

  9. Ishizaka, A., Pearman, C., Nemery, P.: AHPSort: an AHP-based method for sorting problems. Int. J. Prod. Res. 50, 4767–4784 (2012)

    Article  Google Scholar 

  10. Wu, W., Kou, G., Peng, Y., Ergu, D.: Improved AHP-group decision making for investment strategy selection. Technol. Econ. Dev. Econ. 18(2), 299–316 (2012)

    Article  Google Scholar 

  11. Karami, J., Alimohammadi, A., Seifouri, T.: Water quality analysis using a variable consistency dominance-based rough set approach. Comput. Environ. Urban Syst. 43, 25–33 (2014)

    Article  Google Scholar 

  12. Pawlak, Z.: Rough set theory and its applications. J. Telecommun. Inf. Technol. 29, 7–10 (1998)

    MATH  Google Scholar 

  13. Ali, R., Siddiqi, M.H., Lee, S.: Rough set-based approaches for discretization : a compact review (2015)

    Google Scholar 

  14. Błaszczy, J., Greco, S., Matarazzo, B., Słowi, R.: jMAF - Dominance-Based Rough Set Data, pp. 185–209

    Google Scholar 

  15. Hu, Y.C.: Rough sets for pattern classification using pairwise-comparison-based tables. Appl. Math. Model. 37(12–13), 7330–7337 (2013)

    Article  MathSciNet  Google Scholar 

  16. Liang, J., Wang, F., Dang, C., Qian, Y.: An efficient rough feature selection algorithm with a multi-granulation view. Int. J. Approx. Reason. 53(6), 912–926 (2012)

    Article  MathSciNet  Google Scholar 

  17. Vol, F., Computing, O.F., No, D.S., Ciznicki, M., Kurowski, K., We, J.: Evaluation of Selected Resource Allocation Many-Core Processors and Graphics. 3 (2014)

    Google Scholar 

  18. Keeney, R.L.: Decision Analysis: An Overview. Operations Research 30, 803–838 (1982)

    Article  Google Scholar 

  19. Chai, J., Liu, J.N.K.: Dominance-based decision rule induction for multicriteria ranking. Int. J. Mach. Learn. Cybern. 4, 427–444 (2013)

    Article  Google Scholar 

  20. Borgonovo, E., Marinacci, M.: Decision analysis under ambiguity. Eur. J. Oper. Res. 000, 1–14 (2015)

    MathSciNet  MATH  Google Scholar 

  21. Greco, S., Słowiński, R., Zielniewicz, P.: Putting Dominance-based Rough Set Approach and robust ordinal regression together. Decis. Support Syst. 54, 891–903 (2013)

    Article  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  23. Chai, J., Liu, J.N.K., Ngai, E.W.T.: Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Syst. Appl. 40(10), 3872–3885 (2013)

    Article  Google Scholar 

  24. Kavita, Yadav, S.P., Kumar, S.: A Multi-criteria Interval-valued intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS, vol. 5908, pp. 303–312. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  25. Vahdani, B., Hadipour, H., Tavakkoli-Moghaddam, R.: Soft computing based on interval valued fuzzy ANP-A novel methodology. J. Intell. Manuf. 23, 1529–1544 (2012)

    Article  Google Scholar 

  26. Fernandez, E., Lopez, E., Bernal, S., Coello Coello, C., Navarro, J.: Evolutionary multiobjective optimization using an outranking-based dominance generalization. Comput. Oper. Res. 37(2), 390–395 (2010)

    Article  MATH  Google Scholar 

  27. Durbach, I.N., Stewart, T.J.: Modeling uncertainty in multi-criteria decision analysis. Eur. J. Oper. Res. 223(1), 1–14 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  28. Chakhar, S., Saad, I.: Dominance-based rough set approach for groups in multicriteria classification problems. Decis. Support Syst. 54(1), 372–380 (2012)

    Article  Google Scholar 

  29. Velasquez, M., Hester, P.T.: An Analysis of Multi-Criteria Decision Making Methods. Int. J. Oper. Res. 10(2), 56–66 (2013)

    MathSciNet  Google Scholar 

  30. Cinelli, M., Coles, S.R., Kirwan, K.: Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment. Ecol. Indic. 46, 138–148 (2014)

    Article  Google Scholar 

  31. Huang, B., Wei, D., Li, H., Zhuang, Y.: Using a rough set model to extract rules in dominance-based interval-valued intuitionistic fuzzy information systems. Inf. Sci. (Ny) 221, 215–229 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  32. Aydogan, E.K.: Performance measurement model for Turkish aviation firms using the rough-AHP and TOPSIS methods under fuzzy environment. Expert Syst. Appl. 38, 3992–3998 (2011)

    Article  Google Scholar 

  33. Lee, C., Lee, H., Seol, H., Park, Y.: Evaluation of new service concepts using rough set theory and group analytic hierarchy process. Expert Syst. Appl. 39(3), 3404–3412 (2012)

    Article  Google Scholar 

  34. Dutta, M., Husain, Z.: An application of Multicriteria Decision Making to built heritage. The case of Calcutta. J. Cult. Herit. 10, 237–243 (2009)

    Article  Google Scholar 

  35. Devi, K., Yadav, S.P.: A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method. Int. J. Adv. Manuf. Technol. 66, 1219–1229 (2013)

    Article  Google Scholar 

  36. Lee, C.S.: A rough-fuzzy hybrid approach on a Neuro-Fuzzy classifier for high dimensional data. In: Proc. Int. Jt. Conf. Neural Networks, pp. 2764–2769 (2011)

    Google Scholar 

  37. Chai, J., Liu, J.N.K.: Class-based rough approximation with dominance principle. In: Proc. - 2011 IEEE Int. Conf. Granul. Comput. GrC 2011, pp. 77–82 (2011)

    Google Scholar 

  38. Tzeng, K.S.G.: A decision rule-based soft computing model for supporting financial performance improvement of the banking industry (2014)

    Google Scholar 

  39. Liou, J.J.H., Yen, L., Tzeng, G.H.: Using decision rules to achieve mass customization of airline services. Eur. J. Oper. Res. 205(3), 680–686 (2010)

    Article  MATH  Google Scholar 

  40. Hu, M., Shen, F., Chen, Y., Wang, J.: Method of multi-attribute decision analysis based on rough sets dealing with grey information. In: 2011 IEEE Int. Conf. Syst. Man, Cybern., no. 90924022, pp. 1457–1462 (2011)

    Google Scholar 

  41. Hu, M., Shen, F., Chen, Y.: A multi-attribute decision analysis method based on rough sets dealing with uncertain information. In: Proc. 2011 IEEE Int. Conf. Grey Syst. Intell. Serv., pp. 576–581 (2011)

    Google Scholar 

  42. Miao, D., Duan, Q., Zhang, H., Jiao, N.: Rough set based hybrid algorithm for text classification. Expert Syst. Appl. 36(5), 9168–9174 (2009)

    Article  Google Scholar 

  43. Błaszczyński, J., Greco, S., Słowiński, R.: Inductive discovery of laws using monotonic rules. Eng. Appl. Artif. Intell. 25, 284–294 (2012)

    Article  Google Scholar 

  44. Augeri, M.G., Colombrita, R., Greco, S., Lo Certo, A., Matarazzo, B., Slowinski, R.: Dominance-Based Rough Set Approach to Budget Allocation in Highway Maintenance Activities. J. Infrastruct. Syst. 17(June), 75–85 (2011)

    Article  Google Scholar 

  45. Greco, S., Matarazzo, B., Słowinski, R.: Interactive Evolutionary Multiobjective Optimization using Dominance-based Rough Set Approach (2010)

    Google Scholar 

  46. Phillips, L.D.: How Raiffa’s RAND memo led to a multi-criteria computer program 23(July 2013), 3–23 (2006)

    Google Scholar 

  47. Liou, J.J.H.: Variable Consistency Dominance-based Rough Set Approach to formulate airline service strategies. Appl. Soft Comput. J. 11(5), 4011–4020 (2011)

    Article  MathSciNet  Google Scholar 

  48. Chai, J., Liu, J.N.K.: A novel believable rough set approach for supplier selection. Expert Syst. Appl. 41(1), 92–104 (2014)

    Article  Google Scholar 

  49. Capotorti, A., Barbanera, E.: Credit scoring analysis using a fuzzy probabilistic rough set model. Comput. Stat. Data Anal. 56(4), 981–994 (2012)

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. UTM-IRDA Digital Media Center of Excellence, Universiti Teknologi Malaysia, 81310, Johor, Malaysia

    Masurah Mohamad & Ali Selamat

  2. Faculty of Computing, Universiti Teknologi Malaysia, 81310, Johor, Malaysia

    Masurah Mohamad & Ali Selamat

  3. Faculty of Informatics and Management, Center for Basic and Applied Research, University of Hradec Kralove, Rokitanskeho 62, 500 03, Hradec Kralove, Czech Republic

    Ondrej Krejcar & Kamil Kuca

Authors
  1. Masurah Mohamad
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  2. Ali Selamat
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  3. Ondrej Krejcar
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  4. Kamil Kuca
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Corresponding author

Correspondence to Ali Selamat .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  2. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. Computer Science Department, Universidad Autónoma De Madrid, Madrid, Spain

    David Camacho

  4. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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Mohamad, M., Selamat, A., Krejcar, O., Kuca, K. (2015). A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_26

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  • DOI: https://doi.org/10.1007/978-3-319-24306-1_26

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