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References

    1. 1)
      • 1. Pawlak, Z.: ‘Rough sets’, Int. J. Comput. Inf. Sci., 1982, 11, (5), pp. 341356.
    2. 2)
      • 2. Du, W., Hu, B.: ‘Dominance-based rough fuzzy set approach and its application to rule induction’, Eur. J. Oper. Res., 2017, 261, (2), pp. 690703.
    3. 3)
      • 3. Skowinski, A., Vanderpooten, D.: ‘A generalized definition of rough approximations based on similarity’, IEEE Trans. Knowl. Data Eng., 2000, 12, (2), pp. 331336.
    4. 4)
      • 4. Wang, C., Qi, Y., Shao, M., et al: ‘A fitting model for feature selection with fuzzy rough sets’, IEEE Trans. Fuzzy Syst., 2017, 25, (4), pp. 741753.
    5. 5)
      • 5. Tsang, E., Chen, D., Yueng, D., et al: ‘Attributes reduction using fuzzy rough sets’, IEEE Trans. Fuzzy Syst., 2008, 16, (5), pp. 11301141.
    6. 6)
      • 6. Qin, B., Zeng, F., Yan, K.: ‘Knowledge structures in a tolerance knowledge base and their uncertainty measures’, Knowl.-Based Syst., 2018, 151, pp. 198215.
    7. 7)
      • 7. Eugenia, C.M., Medina, J., Ramirez, E.A.: ‘Comparative study of adjoint triples’, Fuzzy Sets Syst., 2013, 211, pp. 114.
    8. 8)
      • 8. Eugenia, C.M., Medina, J., Ramirez, E., et al: ‘Attribute reduction in multi-adjoint concept lattices’, Inf. Sci., 2015, 294, pp. 4156.
    9. 9)
      • 9. Medina, J., Ojeda, M.A.: ‘Multi-adjoint t-concept lattices’, Inf. Sci., 2010, 180, (5), pp. 712725.
    10. 10)
      • 10. Medina, J.: ‘Multi-adjoint property-oriented and object-oriented concept lattices’, Inf. Sci., 2012, 190, pp. 95106.
    11. 11)
      • 11. Medina, J., Eugenia, C.M.: ‘Dual multi-adjoint concept lattices’, Inf. Sci., 2013, 225, pp. 754.
    12. 12)
      • 12. Cornelis, C., Medina, J., Verbiest, N.: ‘Multi-adjoint fuzzy rough sets: definition, properties and attribute selection’, Int. J. Approx. Reason., 2014, 55, (1), pp. 412426.
    13. 13)
      • 13. Zadeh, L.A.: ‘Fuzzy sets’, Inf. Control, 1965, 8, pp. 338353.
    14. 14)
      • 14. Yang, Y., Chen, D., Wang, H., et al: ‘Fuzzy rough set based incremental attribute reduction from dynamic data with sample arriving’, Fuzzy Sets Syst., 2017, 312, pp. 6686.
    15. 15)
      • 15. Atanassov, K.T.: ‘Intuitionistic fuzzy sets’, Fuzzy Sets Syst., 1986, 20, (1), pp. 8796.
    16. 16)
      • 16. Atanassov, K.T.: ‘Intuitionistic fuzzy sets’ (Physica-Verlag, Hedelkery, 1999).
    17. 17)
      • 17. Bustince, H., Burillo, P.: ‘Structures on intuitionistic fuzzy relations’, Fuzzy Sets Syst., 1996, 78, pp. 293303.
    18. 18)
      • 18. Takeuti, G., Titani, S.: ‘Intuitionistic fuzzy logic and intuitionistic fuzzy set theory’, Symb. Log., 1984, 49, (3), pp. 851866.
    19. 19)
      • 19. Jiang, Y., Tang, Y., Wang, J., et al: ‘Reasoning with in intuitionistic fuzzy rough description logics’, Inf. Sci., 2009, 179, (14), pp. 23622378.
    20. 20)
      • 20. Thomas, K., Nair, L.: ‘Rough intuitionistic fuzzy sets in a lattice’, Int. Math. Forum, 2011, 6, (27), pp. 13271335.
    21. 21)
      • 21. Zhang, Y., Yang, X.: ‘Intuitionistic fuzzy dominance-based rough set approach: model and attribute reductions’, J. Soft., 2012, 7, (3), pp. 551563.
    22. 22)
      • 22. Wang, C., Chen, S.: ‘Multiple attribute decision making based on interval-valued intuitionistic fuzzy sets, linear programming methodology, and the extended TOPSIS method’, Inf. Sci., 2017, 397-398, pp. 155167.
    23. 23)
      • 23. Zhang, X., Chen, D., Tsang, E.: ‘Generalized dominance-based rough set model for the dominance intuitionistic fuzzy information systems’, Inf. Sci., 2017, 378, pp. 125.
    24. 24)
      • 24. Xue, Z., Si, X., Xue, T., et al: ‘Multi-granulation covering rough intuitionistic fuzzy sets’, J. Intell. Fuzzy Syst., 2017, 32, (1), pp. 899911.
    25. 25)
      • 25. Tiwari, A., Shreevastava, S., Som, T., et al: ‘Tolerance-based intuitionistic fuzzy-rough set approach for attribute reduction’, Expert Syst. Appl., 2018, 101, pp. 205212.
    26. 26)
      • 26. Huang, B., Guo, C., Zhuang, Y., et al: ‘Intuitionistic fuzzy multigranulation rough sets’, Inf. Sci., 2014, 277, pp. 299320.
    27. 27)
      • 27. Samanta, S., Mondal, T.: ‘Intuitionistic fuzzy rough sets and rough intuitionistic fuzzy sets’, J Fuzzy Math., 2001, 9, pp. 561582.
    28. 28)
      • 28. Zhou, L., Wu, W., Zhang, W.: ‘On characterization of intuitionistic fuzzy rough sets based on intuitionistic fuzzy implicators’, Inf. Sci., 2009, 179, pp. 883898.
    29. 29)
      • 29. Zhou, L., Wu, W.: ‘Characterization of rough set approximations in Atanassov intuitionistic fuzzy set theory’, Comput. Math. Appl., 2011, 62, (1), pp. 282296.
    30. 30)
      • 30. Wu, W., Zhou, L.: ‘On intuitionistic fuzzy topologies based on intuitionistic fuzzy reflexive and transitive relations’, Soft Comput. – Fusion Found. Methodol. Appl., 2011, 15, (6), pp. 11831194.
    31. 31)
      • 31. Zhou, L., Zhang, W., Wu, W.: ‘Roughness measures of intuitionistic fuzzy sets’. Int. Conf. on Rough Sets and Knowledge Technology, Chengdu, China, May 2008 (LNCS, 5009), pp. 308315.
    32. 32)
      • 32. Singh, S., Garg, H.: ‘Distance measures between type-2 intuitionistic fuzzy sets and their application to multicriteria decision-making process’, Appl. Intell., 2017, 46, (4), pp. 788799.
    33. 33)
      • 33. Huang, B., Li, H., Feng, G., et al: ‘Inclusion measure based multi-granulation intuitionistic fuzzy decision-theoretic rough sets and their application to ISSA’, Knowl.-Based Syst., 2017, 138, pp. 220231.
    34. 34)
      • 34. Zhou, L., Wu, W.: ‘On generalized intuitionistic fuzzy rough approximation operators’, Inf. Sci., 2008, 178, (11), pp. 24482465.
    35. 35)
      • 35. Ciobanu, G., Vaideanu, C.: ‘An efficient method to factorize fuzzy attribute-oriented concept lattices’, Fuzzy Sets Syst., 2016, 317, pp. 121132.
    36. 36)
      • 36. Jensen, R., Shen, Q.: ‘Fuzzy-rough data reduction with ant colony optimization’, Fuzzy Sets Syst., 2015, 1, (149), pp. 520.
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