Dominance-based fuzzy rough approach to an interval-valued decision system

Xibei YANG, Ming ZHANG

PDF(158 KB)
PDF(158 KB)
Front. Comput. Sci. ›› 2011, Vol. 5 ›› Issue (2) : 195-204. DOI: 10.1007/s11704-011-0331-4
RESEARCH ARTICLE

Dominance-based fuzzy rough approach to an interval-valued decision system

Author information +
History +

Abstract

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.

Keywords

certainty factor / decision rule / dominance relation / interval-valued information system / interval-valued decision system / fuzzy rough approximation

Cite this article

Download citation ▾
Xibei YANG, Ming ZHANG. Dominance-based fuzzy rough approach to an interval-valued decision system. Front Comput Sci Chin, 2011, 5(2): 195‒204 https://doi.org/10.1007/s11704-011-0331-4

References

[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 (,)–fuzzy rough approximation operators. Fuzzy Sets and Systems, 2005, 154(1): 76-102
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

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 60632050) and Postdoctoral Science Foundation of China (20100481149).

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(158 KB)

Accesses

Citations

Detail

Sections
Recommended

/