Variable precision rough set model over two universes and its properties

Soft Computing

Volumn 15, Issue 3, 2011, Pages 557-567

  Shen, Yonghong ^a   Wang, Faxing ^b  

^a TIANSHUI NORMAL UNIVERSITY   (China)

^b NANJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Approximation operator; Inclusion degree; Pawlak rough sets; Reverse approximation operator; Variable precision rough sets

Indexed keywords

APPROXIMATION ALGORITHMS;

APPROXIMATION OPERATORS; INCLUSION DEGREES; LOWER AND UPPER APPROXIMATIONS; PAWLAK ROUGH SET; ROUGH SET MODELS; TWO PARAMETER; VARIABLE PRECISION ROUGH SETS; VARIABLE-PRECISION ROUGH SET MODEL;

ROUGH SET THEORY;

EID: 79951775060     PISSN: 14327643     EISSN: 14337479     Source Type: Journal    
DOI: 10.1007/s00500-010-0562-6     Document Type: Article

References (21)

1. Gong ZT, Sun BZh (2008) Probability rough sets model between different universes and its applications. Proceedings of the seventh International Conference on machine learning and cybernetics. IEEE, Kunming, pp 561-565.
2. Gu ShM, Gao J, Tian XQ (2007) A fuzzy measure based on variable precision rough sets. In: Cao BY (ed) Fuzzy information and engineering (ICFIE), ASC, vol 40, pp 798-807.
3. Inuiguchi M (2004a) Generalizations of rough sets: from crisp to fuzzy cases. In: Tsumoto S et al (eds) RSCTC2004, LNAI 3066, pp 26-37.
4. Inuiguchi M (2004b) Generalizations of rough sets and rule extraction. In: Peters JF et al (ed) Transactions on rough sets I, LNCS 3100, pp 96-119.
5. Kryszkiewicz M (1999) Rules in incomplete information systems. Info Sci 113: 271-292.
6. Mieszkowicz-Rolka A, Rolka L (2004a) Variable precision fuzzy rough sets. In: Peters JF et al (ed) Transactions on rough sets I. LNCS 3100, pp 144-160.
7. Mieszkowicz-Rolka A, Rolka L (2004b) Remarks on approximation quality in variable precision fuzzy rough sets model. In: Tsumoto S et al (ed) RSCTC 2004, LNAI 3066, pp 402-411.
8. Mieszkowicz-Rolka A, Rolka L (2004c) Fuzzy implication operators in variable precision fuzzy rough sets model. In: Rutkowski L et al (ed) ICAISC 2004, LNAI 3070, pp 498-503.
9. Mieszkowicz-Rolka A, Rolka L (2005) Variable precision fuzzy rough sets model in the analysis of process data. In: Slezak D et al (ed) RSFDGrC 2005, LNAI 3641, pp 354-363.
10. Pawlak Z (1982) Rough sets. Int J Comput Info Sci 11: 341-356.
11. Pei DW, Xu ZB (2004) Rough set models on two universes. Int J Gen Syst 33: 569-581.
12. Shu L, He XZh (2007) Rough set model with double universe of discourse. Information reuse and integration(IRI 2007), IEEE, pp 492-495.
13. Wang YP, Wang WZ (2008) Constructive and algebraic methods of approximation operators over two universes. Fuzzy Syst Math (Chinese Version) 22: 151-154.
14. Yao YY (1998) Generalized rough set models. In: Polkowski L, Skowron A (eds) Rough sets in knowledge discovery. Physica-Verlag, Heidelberg, pp 286-328.
15. Yao YY (2003) On generalizing rough set theory. In: Rough sets, fuzzy sets, and granular computing. Proceedings of the 9th International Conference (RSFDGrC 2003), LNAI 2639, pp 44-51.
16. Yao YY, Lin TY (1996) Generalization of rough sets using modal logics. Intell Autom Soft Comput 2: 103-120.
17. Yao YY, Wong SKM (1992) A decision theoretic framework for approximating concepts. Int J Man Machine Stud 37: 793-809.
18. Yao YY, Wong SKM (1996) Generalized probabilistic rough set models. In: Chen YY, Hirota K, Yen JY (eds) Soft computing in intelligent systems and information processing. Proceedings of 1996 Asian Fuzzy Systems Symposium. IEEE Press, pp 158-163.
19. Yao YY (2008) Probabilistic rough set approximations. Int J Approx Reason 49: 255-271.
20. Zhang WX, Leung Y (1996) Theory of including degrees and its applications to uncertainty inferences. Soft computing in intelligent systems and information processing. IEEE, New York, pp 496-501.
21. Ziarko W (1993) Variable precision rough set model. J Comput Syst Sci 46: 39-59.