Dominance-based fuzzy rough approach to an interval-valued decision system
Xibei YANG, Ming ZHANG
Dominance-based fuzzy rough approach to an interval-valued decision system
Though the dominance-based rough set approach has been applied to interval-valued information systems for knowledge discovery, the traditional dominance relation cannot be used to describe the degree of dominance principle in terms of pairs of objects. In this paper, a ranking method of interval-valued data is used to describe the degree of dominance in the interval-valued information system. Therefore, the fuzzy rough technique is employed to construct the rough approximations of upward and downward unions of decision classes, from which one can induce at least and at most decision rules with certainty factors from the interval-valued decision system. Some numerical examples are employed to substantiate the conceptual arguments.
certainty factor / decision rule / dominance relation / interval-valued information system / interval-valued decision system / fuzzy rough approximation
[1] |
Pawlak Z. Rough Sets–Theoretical Aspects of Reasoning About Data.Boston: Kluwer Academic Publishers Press, 1991
|
[2] |
Greco S, Matarazzo B, Słowiński R. Rough approximation by dominance relations. International Journal of Intelligent Systems, 2002, 17(2): 153-171
CrossRef
Google scholar
|
[3] |
Greco S, Matarazzo B, Słowiński R. Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 2001, 129(1): 1-47
CrossRef
Google scholar
|
[4] |
Błaszczyński J, Greco S, Słowiński R. Multi-criteria classification–a new scheme for application of dominance-based decision rules. European Journal of Operational Research, 2007, 181(3): 1030-1044
CrossRef
Google scholar
|
[5] |
Yang X, Yang J, Wu C, Yu D. Dominance-based rough set approach and knowledge reductions in incomplete ordered information system. Information Sciences, 2008, 178(4): 1219-1234
CrossRef
Google scholar
|
[6] |
Yang X, Xie J, Song X, Yang J. Credible rules in incomplete decision system based on descriptors. Knowledge-Based Systems, 2009, 22(1): 8-17
CrossRef
Google scholar
|
[7] |
Yang X, Yu D, Yang J, Wei L. Dominance-based rough set approach to incomplete interval-valued information system. Data & Knowledge Engineering, 2009, 68(11): 1331-1347
CrossRef
Google scholar
|
[8] |
Shao M, Zhang W. Dominance relation and rules in an incomplete ordered information system. International Journal of Intelligent Systems, 2005, 20(1): 13-27
CrossRef
Google scholar
|
[9] |
Greco S, Inuiguchi M, Słowiński R. Fuzzy rough sets and multiple–premise gradual decision rules. International Journal of Approximate Reasoning, 2006, 41(2): 179-211
CrossRef
Google scholar
|
[10] |
Greco S, Matarazzo B, Słowiński R. Dominance-based rough set approach to case–based reasoning. In: Proceedings of 3rd International Conference on Modeling Decisions for Artificial Intelligence. 2006, 7-18
|
[11] |
Greco S, Matarazzo B, Słowiński R. Fuzzy set extensions of the dominance-based rough set approach. In: Sola H, Herrera F, Montero J, eds. Fuzzy Sets and Their Extensions: Representation, Aggregation and Models.Berlin: Springer-Verlag, 2008, 239-261
|
[12] |
Błaszczyński J, Greco S, Słowiński R. On variable consistency dominance-based rough set approaches. In: Proceedings of 5th International Conference on Rough Sets and Current Trends in Computing. 2006, 191-202
|
[13] |
Błaszczyński J, Greco S, Słowiński R. Monotonic variable consistency rough set approaches. In: Proceedings of 2nd International Conference on Rough Sets and Knowledge Technology. 2007, 126-133
|
[14] |
Qian Y, Dang C, Liang J, Tang D. Set-valued ordered information systems. Information Sciences, 2009, 179(16): 2809-2832
CrossRef
Google scholar
|
[15] |
Fan T, Liu D, Tzeng G. Rough set-based logics for multicriteria decision analysis. European Journal of Operational Research, 2007, 182(1): 340-355
CrossRef
Google scholar
|
[16] |
Qian Y, Liang J, Dang C. Interval ordered information systems. Computers & Mathematics with Applications, 2008, 56(8): 1994-2009
CrossRef
Google scholar
|
[17] |
Dembczyński K, Greco S, Słowiński R. Rough set approach to multiple criteria classification with imprecise evaluations and assignments. European Journal of Operational Research, 2009, 198(2): 626-636
CrossRef
Google scholar
|
[18] |
Da Q, Liu X. Interval number linear programming and its satisfactory solution. Systems Engineering – Theory & Practice, 1999, 19(4): 3-7
|
[19] |
Facchinetti G, Ricci R, Muzzioli S. Note on ranking fuzzy triangular numbers. International Journal of Intelligent Systems, 1998, 13(7): 613-622
CrossRef
Google scholar
|
[20] |
Sengupta A, Pal T. On comparing interval numbers. European Journal of Operational Research, 2000, 127(1): 28-43
CrossRef
Google scholar
|
[21] |
Bhatt R, Gopal M. On the compact computational domain of fuzzy–rough sets. Pattern Recognition Letters, 2005, 26(11): 1632-1640
CrossRef
Google scholar
|
[22] |
Dubois D, Prade H. Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems, 1990, 17(2): 191-209
CrossRef
Google scholar
|
[23] |
Li T. Rough approximation operators on two universes of discourse and their fuzzy extensions. Fuzzy Sets and Systems, 2008, 159(22): 3033-3050
CrossRef
Google scholar
|
[24] |
Nanda S, Majumdar S. Fuzzy rough sets. Fuzzy Sets and Systems, 1992, 45(2): 157-160
CrossRef
Google scholar
|
[25] |
Wu W, Mi J, Zhang W. Generalized fuzzy rough sets. Information Sciences, 2003, 151: 263-282
CrossRef
Google scholar
|
[26] |
Wu W, Zhang W. Constructive and axiomatic approaches of fuzzy approximation operators. Information Sciences, 2004, 159(3-4): 233-254
CrossRef
Google scholar
|
[27] |
Wu W, Leung Y, Mi J. On characterizations of
CrossRef
Google scholar
|
[28] |
Yeung D, Chen D, Tsang E, Lee J, Wang X. On the generalization of fuzzy rough sets. IEEE Transactions on Fuzzy Systems, 2005, 13(3): 343-361
CrossRef
Google scholar
|
/
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