[1] M. Alamuri, B. R. Surampudi and A. Negi, A survey of distance/similarity measures for
categorical data, In 2014 International Joint Conference on Neural Networks, IJCNN (2014),
1907-1914.
[2] N. Azam and J. T. Yao, Analyzing uncertainties of probabilistic rough set regions with game-
theoretic rough sets, International Journal of Approximate Reasoning, 55(1) (2014), 142-155,
2014.
[3] C. Cornelis, M. De Cock and A. M. Radzikowska, Fuzzy rough sets: from theory into practice,
Handbook of Granular computing, (2008), 533-552.
[4] B. Davvaz, A short note on algebraic T-rough sets, Information Sciences, 178 (2008), 3247-
3252.
[5] B. Davvaz, Approximations in n-ary algebraic systems, Soft Computing, 12(4) (2008), 409-
418.
[6] B. Davvaz, Approximations in a semigroup by using a neighbourhood system, International
Journal of Computer Mathematics, 88(4) (2011), 709-713.
[7] B. Davvaz and M. Mahdavipour, Rough approximations in a general approximation space
and their fundamental properties, International Journal of General Systems, 37(3) (2008),
373{386.
[8] A. P. Dempster, Upper and lower probabilities induced by a multivalued mapping, The Annals
of Mathematical Statistics, 38(2) (1967), 325{339.
[9] D. Dubois and H. Prade, Rough fuzzy sets and fuzzy rough sets, International Journal of
General Systems, 17(2-3) (1990), 191{209.
[10] A. Gomolinska, Approximation spaces based on relations of similarity and dissimilarity of
objects, Fundamenta Informaticae, 79(3) (2007), 319{333.
[11] A. Gomolinska, On certain rough inclusion functions, Transactions on Rough Sets IX,
Springer Berlin Heidelberg, (2008), 35{55.
[12] A. Gomolinska, Rough approximation based on weak q-RIFs, Transactions on Rough Sets X,
Springer Berlin Heidelberg, (2009), 117{135.
[13] A. Gomolinska and M.Wolski, Rough inclusion functions and similarity indices, Fundamenta
Informaticae, 133(2) (2014), 149{163.
[14] S. Greco, B. Matarazzo and R. Slowinski, Rough approximation of a preference relation by
dominance relations, European Journal of Operational Research, 117(1) (1999), 63{83.
[15] J. W. Grzymala Busse, Knowledge acquisition under uncertainty { a rough set approach,
Journal of Intelligent and Robotic Systems, 1(1) (1988), 3{16.
[16] J. W. Grzymala Busse, Rough-set and Dempster-Shafer approaches to knowledge acquisition
under uncertainty { a comparison, Technical report, University of Kansas, 1987.
[17] M. R. Hooshmandasl, A. Shakiba, A. K. Goharshady and A. Karimi, S-approximation: A
new approach to algebraic approximation, Journal of Discrete Mathematics, 2014 (2014),
1{5.
[18] M. J. Lesot, M. Rifqi and H. Benhadda, Similarity measures for binary and numerical data:
a survey, International Journal of Knowledge Engineering and Soft Data Paradigms, 1(1)
(2009), 63{84.
[19] T. J. Li, Rough approximation operators on two universes of discourse and their fuzzy ex-
tensions, Fuzzy Sets and Systems, 159(22) (2008), 3033{3050.
[20] P. Lingras and R. Jensen, Survey of rough and fuzzy hybridization, IEEE International Fuzzy
Systems Conference, (2007), 1{6.
[21] C. Liu, D. Miao and N. Zhang, Graded rough set model based on two universes and its
properties, Knowledge-Based Systems, 33(0) (2012), 65 { 72.
[22] G. Liu, Rough set theory based on two universal sets and its applications, Knowledge-Based
Systems, 23(2) (2010), 110{1150.
[23] N. N. Morsi and M. M. Yakout, Axiomatics for fuzzy rough sets, Fuzzy Sets and Systems,
100(1) (1998), 327{342.
[24] A. Nakamura, Fuzzy rough sets, Note on Multiple-valued Logic in Japan, 9(8) (1988), 1{8.
[25] S. Nanda and S. Majumdar, Fuzzy rough sets, Fuzzy Sets and Systems, 45(2) (1992), 157{
160.
[26] S. K. Pal, L. Polkowski and A. Skowron, Rough-Neural Computing: Techniques for Comput-
ing with Words, Arti cial intelligence, Springer Berlin Heidelberg, 2004.
[27] Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning About Data, Kluwer Academic
Publishers, 1991.
[28] Z. Pawlak and A. Skowron, Rough membership functions, Advances in the Dempster-Shafer
Theory of Evidence, John Wiley & Sons, (1994), 251{271.
[29] Z. Pawlak, S. K. M. Wong, and W. Ziarko, Rough sets: Probabilistic versus deterministic
approach, International Journal of Man-Machine Studies, 29 (1988), 81{95.
[30] Z. Pawlak, Rough sets, International Journal of Computer and Information Sciences, 11
(1982), 341{356.
[31] Z. Pawlak, Vagueness and uncertainty: A rough set perspective, Computational Intelligence,
11(2) (1995), 227{232.
[32] Z. Pawlak, Rough set theory and its applications to data analysis, Cybernetics & Systems,
29(7) (1998), 661{688.
[33] Z. Pei and Z. B. Xu, Rough set models on two universes, International Journal of General
Systems, 33(5) (2004), 569{581.
[34] L. Polkowski, Rough sets: Mathematical foundations, Physica Verlag, 15 (2002).
[35] L. Polkowski and A. Skowron, Rough mereology: A new paradigm for approximate reasoning,
International Journal of Approximate Reasoning, 15(4) (1996), 333{365.
[36] L. Polkowski and A. Skowron, Rough mereology, Methodologies for Intelligent Systems,
Springer Berlin Heidelberg, (1994), 85{94.
[37] A. M. Radzikowska and E. E. Kerre, A comparative study of fuzzy rough sets, Fuzzy Sets and
Systems, 126(2) (2002), 137{155.
[38] A. M. Radzikowska and E. E. Kerre, A comparative study of fuzzy rough sets, Fuzzy Sets and
Systems, 126(2) (2002), 137{155.
[39] G. Shafer, A mathematical theory of evidence, Princeton university press Princeton, 1 (1976).
[40] A. Shakiba and M. R. Hooshmandasl, S-approximation spaces: A three-way decision ap-
proach, Fundamenta Informaticae, 139(3) (2015), 307{328.
[41] A. Shakiba and M. R. Hooshmandasl, Neighborhood system S-approximation spaces and
applications, Knowledge and Information Systems, (2015), 1{46.
[42] Y. Shen and F. Wang, Variable precision rough set model over two universes and its proper-
ties, Soft Computing, 15(3) (2011),557{567.
[43] A. Skowron and J. Stepaniuk, Tolerance approximation spaces, Fundamenta Informaticae,
27(2) (1996), 245{253.
[44] R. Slowinski and D. Vanderpooten, A generalized de nition of rough approximations based on
similarity, IEEE Transactions on Knowledge and Data Engineering, 12(2) (2000), 331{336.
[45] B. Sun, Z. Gong and D. Chen, Fuzzy rough set theory for the interval-valued fuzzy information
systems, Information Sciences, 178(13) (2008), 2794{2815.
[46] B. Sun and W. Ma, Fuzzy rough set model on two di erent universes and its application,
Applied Mathematical Modelling, 35(4) (2011), 1798{1809.
[47] S. K. M. Wong, L. S. Wang, and Y. Y. Yao, Interval structure: A framework for representing
uncertain information, Proceedings of the Eighth International Conference on Uncertainty
in Arti cial Intelligence, UAI'92 (1992), 336{343.
[48] W. Z.Wu, J. Sheng Mi and W. Xiu Zhang, Generalized fuzzy rough sets, Information Sciences,
151 (2003), 263{282.
[49] W. Xu, W. Sun, Y. Liu and W. Zhang, Fuzzy rough set models over two universes, Interna-
tional Journal of Machine Learning and Cybernetics, 4(6) (2013), 631{645.
[50] S. Yamak, O. Kazanci and B. Davvaz, Generalized lower and upper approximations in a ring,
Information Sciences, 180(9) (2010), 1759{1768.
[51] R. Yan, J. Zheng, J. Liu and C. Qin, Rough set over dual-universes in fuzzy approximation
space, Iranian Journal of Fuzzy Systems, 9(3) (2012), 79{91
[52] Q. Yang and X. Wu, 10 challenging problems in data mining research, International Journal
of Information Technology & Decision Making, 5(4) (2006), 597{604.
[53] Y. Y. Yao, A comparative study of fuzzy sets and rough sets, Information Sciences, 109(14)
(1998), 227{242.
[54] Y. Y. Yao, Probabilistic approaches to rough sets, Expert Systems, 20(5) (2003), 287{297.
[55] Y. Y. Yao and P. J. Lingras, Interpretations of belief functions in the theory of rough sets,
Information Sciences, 104(1-2) (1998), 81{106.
[56] Y. Y. Yao, Probabilistic rough set approximations, International Journal of Approximate
Reasoning, 49(2) (2008), 255{271.
[57] Y. Y. Yao, Three-way decision: an interpretation of rules in rough set theory, International
Conference on Rough Sets and Knowledge Technology, Springer Berlin Heidelberg, (2009),
642{649.
[58] Y. Y. Yao, An outline of a theory of three-way decisions, Rough Sets and Current Trends in
Computing, Springer Berlin Heidelberg, (2012), 1{17.
[59] Y. Y. Yao and X. Deng, Quantitative rough sets based on subsethood measures, Information
Sciences, 267 (2014), 306{322.
[60] Y. Y. Yao, Combination of rough and fuzzy sets based on -level sets, Rough sets and Data
Mining, Springer, (1997), 301{321.
[61] Y. Y. Yao, Generalized rough set models, Rough Sets in Knowledge Discovery 1: Methodology
and Approximations, Studies in Fuzziness and Soft Computing, Physica Verlag Heidelberg,
1 (1998), 286{318.
[62] Y. Y. Yao and T. Y. Lin, Generalization of rough sets using modal logic, Intelligent Automa-
tion and Soft Computing, 2(2) (1996), 103{120.
[63] L. A. Zadeh, Fuzzy sets, Information and Control, 8(3) (1965), 338{353.
[64] L. A. Zadeh, The role of fuzzy logic in the management of uncertainty in expert systems,
Fuzzy Sets and Systems, 11(1) (1983), 197{198.
[65] L. A. Zadeh, A simple view of the Dempster-Shafer theory of evidence and its implication
for the rule of combination, AI magazine, 7(2) (1986), 85{90.
[66] H. Y. Zhang, W. X. Zhang and W. Z. Wu, On characterization of generalized interval-
valued fuzzy rough sets on two universes of discourse, International Journal of Approximate
Reasoning, 51(1) (2009), 56{70.
[67] W. X. Zhang and Y. Leung, Theory of including degrees and its applications to uncertainty
inferences, Fuzzy Systems Symposium, Soft Computing in Intelligent Systems and Informa-
tion Processing., Proceedings of the 1996 Asian, (1996), 496{501.
[68] W. Zhu and F. Y.Wang, A new type of covering rough set, 3rd International IEEE Conference
on Intelligent Systems, (2006), 444{449.
[69] W. Zhu and F. Y. Wang, On three types of covering-based rough sets, IEEE Transactions on
Knowledge and Data Engineering, 19(8) (2007), 1131{1144.
[70] W. Zhu and F. Y. Wang, The fourth type of covering-based rough sets, Information Sciences,
201 (2012), 80{92.
[71] W. Ziarko, Variable precision rough set model, Journal of Computer and System Sciences,
46(1) (1993), 39{59.
[72] W. Ziarko, Probabilistic decision tables in the variable precision rough set model, Computa-
tional Intelligence, 17(3) (2001), 593{603.
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