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On General Framework of Type-1 Membership Function Construction: Case Study in QoS Planning

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Abstract

Fuzzy approaches that are proposed to describe uncertain, impressive or vague concepts, are based on the construction of membership function (MF), which reflects what is known about the linguistic variables in the application domain. However, a non-trivial problem exists in how to construct the most appropriate MF that has the best-fit representation of the analysed problem. Therefore, many authors propose their own ways to construct MF using a certain technique in a particular application domain. Consequently, the need for a general approach for constructing MF led us to systematise and to generalise the analysed approaches into a general methodological framework (GMF) of constructing MF. The novelty of this paper is that the proposed GMF is general, domain independent and free of a chosen understanding of fuzziness (i.e., similarity (imprecision), preference (vagueness), and uncertainty). To verify the proposed GMF, it was applied for the enterprise business service quality (QoSEBS) planning problem. The obtained results showed that a semi-automatic MF construction for QoSEBS planning was more sensitive, less subjective and more precise than a manual construction. Moreover, illustrative examples showed that our proposed GMF is applicable and implementable. The reliability of the results was assessed using experts and users’ experience, which is based on general guidelines of the “acceptable” response time limits for various activities.

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References

  1. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    MATH  Google Scholar 

  2. Medasani, S., Kim, J., Krishnapuram, R.: An overview of membership function generation techniques for pattern recognition. Int. J. Approx. Reason. 19(3–4), 391–417 (1998)

    MathSciNet  MATH  Google Scholar 

  3. dos Santos Schwaab, A.A., Nassar, S.M., de Freitas Filho, P.J.: Automatic methods for generation of type-1 and interval type-2 fuzzy membership functions. J. Comput. Sci. 11(9), 976–987 (2015)

    Google Scholar 

  4. Dubois, D., Ostasiewicz, W., Prade, H.: Fuzzy sets: history and basic notions. In: Dubois, D., Prade, H. (eds.) Fundamentals of Fuzzy Sets, pp. 21–124. Springer, Boston (2000)

    MATH  Google Scholar 

  5. Amini, A., Nikraz, N.: A method for constructing non-isosceles triangular fuzzy numbers using frequency histogram and statistical parameters. Soft Comput. Civil Eng. 1(1), 65–85 (2017)

    Google Scholar 

  6. Yadav, H.B., Yadav, D.K.: Construction of membership function for software metrics. Proc. Comput. Sci. 46, 933–940 (2015)

    Google Scholar 

  7. Pazhoumand-Dar, H., Lam, C., Masek, M.: Automatic generation of fuzzy membership functions using adaptive mean-shift and robust statistics. In: Proc. of the 8th international conference on agents and artificial intelligence, pp. 160–171 (2016)

  8. Ferreyra, E., Hagras, H., Mohamed, A., Owusu, G.: A type-2 fuzzy logic system for engineers estimation in the workforce allocation domain. In Proc. of the 2017 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp. 1–6. IEEE, Naples (2017, July)

  9. Nguyen, T., Khosravi, A., Creighton, D., Nahavandi, S.: Medical data classification using interval type-2 fuzzy logic system and wavelets. Appl. Soft Comput. 30, 812–822 (2015)

    Google Scholar 

  10. Wang, H., Yu, C., Wang, L., Yu, Q.: Effective bigdata-space service selection over trust and heterogeneous QoS preferences. IEEE Trans. Serv. Comput. 11(4), 644–657 (2018)

    Google Scholar 

  11. Si, G., Liao, H., Yu, D., Llopis-Albert, C.: Interval-valued 2-tuple hesitant fuzzy linguistic term set and its application in multiple attribute decision making. J. Intell. Fuzzy Syst. 34(6), 4225–4236 (2018)

    Google Scholar 

  12. Jusoh, A., Mardani, A., Omar, R., Štreimikienė, D., Khalifah, Z., Sharifara, A.: Application of MCDM approach to evaluate the critical success factors of total quality management in the hospitality industry. J. Bus. Econ. Manage. 19(2), 399–416 (2018)

    Google Scholar 

  13. Ghorabaee, M., Zavadskas, E., Amiri, M., Esmaeili, A.: Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. J. Clean. Prod. 137, 213–229 (2016)

    Google Scholar 

  14. Litake, S., Prachi, M.: Fuzzification of context parameters for network selection in heterogeneous wireless environment. In: Smys, S., Bestak, R., Chen, J.Z., Kotuliak, I. (eds.), International conference on computer networks and communication technologies. LNDECT 15. pp. 907–921. Springer, Singapore (2019)

  15. Arun, N., Mohan, B.: Mathematical modelling of the simplest fuzzy two-input two-output proportional integral or proportional derivative controller via Larsen product inference. Int. J. Autom. Control 10(1), 34–51 (2016)

    Google Scholar 

  16. Bigand, A., Colot, O.: Membership function construction for interval-valued fuzzy sets with application to Gaussian noise reduction. Fuzzy Sets Syst. 286, 66–85 (2016)

    MathSciNet  Google Scholar 

  17. Rhimi, F., Yahia, S.B., Ahmed, S.B. Balancing between local and global optimization of web services composition by a fuzzy transactional-aware approach. ICSOFT-PT, 75–82 (2016)

  18. Wang, P., Chao, K., Lo, C.: Satisfaction-based Web service discovery and selection scheme utilizing vague sets theory. Inf. Syst. Front. 17(4), 827–844 (2015)

    Google Scholar 

  19. Choi, B., Rhee, F.: Interval type-2 fuzzy memberships function generation methods for pattern recognition. Inf. Sci. 179(13), 2102–2122 (2009)

    MATH  Google Scholar 

  20. Liao, H., Wu, X., Keikha, A., Hafezalkotob, A.: Power average-based score function and extension rule of hesitant fuzzy set and the hesitant power average operators. J. Intell. Fuzzy Syst. 35(3), 3873–3882 (2018)

    Google Scholar 

  21. Mardani, A., Nilashi, M., Zavadskas, E., Awang, S., Zare, H., Jamal, N.: Decision making methods based on fuzzy aggregation operators: three decades review from 1986 to 2017. Int. J. Inf. Technol. Decis. Mak. 17(2), 391–466 (2018)

    Google Scholar 

  22. Krishankumar, R., Ravichandran, K., Premaladha, J., Kar, S., Zavadskas, E., Antucheviciene, J.: A decision framework under a linguistic hesitant fuzzy set for solving multi-criteria group decision making problems. Sustainability 10(8), 2608 (2018)

    Google Scholar 

  23. Mardani, A., Nilashi, M., Zavadskas, E.K., Awang, S.R., Zare, H., Jamal, N.M.: Decision making methods based on fuzzy aggregation operators: three decades review from 1986 to 2017. Int. J. Inf. Technol. Decis. Mak. 17(02), 391–466 (2018)

    Google Scholar 

  24. Vaidya, A., Metkewar, P., Naik, S.: A new paradigm for generation of fuzzy membership function. In Proc. of the 2018 IEEE 8th international advance computing conference (IACC), pp. 1–6. IEEE (2019, April)

  25. Ghorabaee, M., Amiri, M., Zavadskas, E.K., Antucheviciene, J.: A new hybrid fuzzy MCDM approach for evaluation of construction equipment with sustainability considerations. Arch. Civil Mech. Eng. 18(1), 32–49 (2018)

    Google Scholar 

  26. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z., Antucheviciene, J.: A dynamic fuzzy approach based on the EDAS method for multi-criteria subcontractor evaluation. Information 9(3), 68 (2018)

    MATH  Google Scholar 

  27. Luo, W., Zhang, D., Jiang, H., Ni, L., Hu, Y.: Local community detection with the dynamic membership function. IEEE Trans. Fuzzy Syst. 26(5), 3136–3150 (2018)

    Google Scholar 

  28. Tripathy, A.K., Tripathy, P.K.: Fuzzy QoS requirement-aware dynamic service discovery and adaptation. Appl. Soft Comput. 68, 136–146 (2018)

    Google Scholar 

  29. Zheng, H., Feng, Y., Tan, J.: A fuzzy QoS-aware resource service selection considering design preference in cloud manufacturing system. Int. J. Adv. Manuf. Technol. 84(1–4), 371–379 (2016)

    Google Scholar 

  30. Georgieva, O., Petrova-Antonova, D.: Web service selection based on integrated QoS assesment. The ICCGI 2015: tenth international multi-conference on computing in the global information technology, pp. 114–118. IARIA (2015)

  31. Wang, C., Qu, A.: The applications of vague soft sets and generalized. Acta Mathematicae Applicatae Sinica, English Series 31(4), 977–990 (2015)

    MathSciNet  MATH  Google Scholar 

  32. Paul, A. K., Shill, P. C., Rabin, M. R., Kundu, A. M.: Fuzzy membership function generation using DMS-PSO for the diagnosis of heart disease. In: Proc. of the 18th international conference on computer and information technology (ICCIT), pp. 456–461. IEEE (2015, December)

  33. Liao, H., Xu, Z., Zeng, X.J., Xu, D.L.: An enhanced consensus reaching process in group decision making with intuitionistic fuzzy preference relations. Inf. Sci. 329, 274–286 (2016)

    Google Scholar 

  34. Maheswari, S., Karpagam, G.R.: Enhancing fuzzy topsis for web service selection. Int. J. Comput. Appl. Technol. 51(4), 344–351 (2015)

    Google Scholar 

  35. Kumar, R.R., Mishra, S., Kumar, C.: Prioritizing the solution of cloud service selection using integrated MCDM methods under fuzzy environment. J. Supercomput. 73(11), 4652–4682 (2017)

    Google Scholar 

  36. Bagga, P., Joshi, A., Hans, R.: QoS based web service selection and multi-criteria decision making methods. Int. J. Interact. Multimed. Artif. Intell. 5(4), 113–121 (2019)

    Google Scholar 

  37. Miliauskaitė, J.: Some methodological issues related to preliminary QoS. Balt. J. Mod. Comput. 3(3), 149–163 (2015)

    Google Scholar 

  38. Chouiref, Z., Belkhir, A., Benouaret, K., Hadjali, A.: A fuzzy framework for efficient user-centric web service selection. Appl. Soft Comput. 41, 51–65 (2016)

    Google Scholar 

  39. Xu, J., Guo, L., Zhang, R., Hu, H., Wang, F., Pei, Z.: QoS-aware service composition using fuzzy set theory and genetic algorithm. Wireless Pers. Commun. 102(2), 1009–1028 (2018)

    Google Scholar 

  40. Zhang, S., Xu, Y., Zhang, W., Yu, D.: A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm. J. Intell. Manuf. 30(5), 2069–2083 (2019)

    Google Scholar 

  41. Lupeikienė, A., Miliauskaitė, J., Čaplinskas, A.: A model of view-based enterprise business service quality evaluation framework. Informatica 24(4), 543–560 (2013)

    Google Scholar 

  42. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 8(3):199–249(I) (1975); 8(4):301–357(II) (1975); 9(1):43–80 (1975)

  43. Zimmermann, H.J.: Fuzzy set theory—and its applications. Springer Science & Business Media, Berlin (2011)

    Google Scholar 

  44. Zanotelli, R., Reiser, R., Bedregal, B.: n-dimensional intervals and fuzzy S-implications. In Proc. of the 2018 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp. 1-8. IEEE (2018, July)

  45. Hatzimichailidis, A., Papakostas, G., Kaburlasos, V.: On constructing distance and similarity measures based on fuzzy implications. In: Papakotas, G., Hatzimichailidis, A., Kaburlasos, V. (eds.) Handbook of fuzzy sets comparison—theory, algorithms and applications, 6th edn, pp. 1–21. Science Gate Publishing, Xanthi (2016)

    Google Scholar 

  46. Mohibullah, M., Hossain, M., Hasan, M.: Comparison of Euclidean distance function and Manhattan distance function using k-mediods. Int. J. Comput. Sci. Inf. Secur. 13(10), 61 (2015)

    Google Scholar 

  47. Kaufmann, M., Meier, A., Stoffel, K.: IFC-filter: membership function generation for inductive fuzzy classification. Expert Syst. Appl. 42(21), 8369–8379 (2015)

    Google Scholar 

  48. Bilgiç, T., Türkşen, I.: Measurement of membership functions: theoretical and empirical work. In: Dubois, D., Prade, H. (eds.) Fundamentals of fuzzy sets, 7th edn, pp. 195–227. Springer, Berlin (2000)

    MATH  Google Scholar 

  49. Bilgiç, T., Turksen, I.: Elicitation of Membership Functions: How far can theory take us? In Proc. of the Sixth IEEE international conference on fuzzy systems, 3, pp. 1321–1325. Barcelona (1997)

  50. Klir, G., Yuan, B.: Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, Upper Saddle River (1995)

    MATH  Google Scholar 

  51. Hasuike, T., Katagiri, H.: Construction of an appropriate membership function based on size of fuzzy set and mathematical programming. In: Proc. of the international multiconference of engineers and computer scientists, 2 (2016)

  52. Schuerz, M., Adlassnig, K.-P., Lagor, C., Schneider, B., Grabner, G.: Definition of fuzzy sets representing medical concepts and acquisition of fuzzy relationships between them by semi-automatic procedures. (Electronic Newsletter) Fuzzy Soft Comput Digest 1(2) (1999)

  53. Richardson, J.: The concepts and methods of phenomenographic research. Rev. Educ. Res. 69(1), 53–82 (1999)

    Google Scholar 

  54. Vafaei, N., Ribeiro, R.A., Camarinha-Matos, L.M.: Normalization techniques for multi-criteria decision making: analytical hierarchy process case study. In: Doctoral conference on computing, electrical and industrial systems, pp. 261–269. Springer, Cham (2016, April)

  55. Deza, M., Deza, E.: Encyclopedia of distances. Springer, Berlin (2009)

    MATH  Google Scholar 

  56. Nielsen, J.: Usability engineering. Elsevier, New York (1994)

    MATH  Google Scholar 

  57. Taylor, B., Dey, A., Siewiorek, D., Smailagic, A.: Using crowd sourcing to measure the effects of system response delays on user engagement. In: Proc. of the 2016 CHI conference on human factors in computing systems, pp. 4413–4422. ACM (2016, May)

  58. Wu, D.: Approaches for reducing the computational cost of interval type-2 fuzzy logic systems: overview and comparisons. IEEE Trans Fuzzy Syst 21(1), 80–99 (2013)

    Google Scholar 

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Miliauskaitė, J., Kalibatiene, D. On General Framework of Type-1 Membership Function Construction: Case Study in QoS Planning. Int. J. Fuzzy Syst. 22, 504–521 (2020). https://doi.org/10.1007/s40815-019-00753-4

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