L-Fuzzy Rough Approximation Operators Based on Co-Implication and Their (Single) Axiomatic Characterizations
Abstract
:1. Introduction
- ⋄
- Let be a complete residuated lattice. Based on L-fuzzy relations, Radzikowska [40] defined an L-fuzzy upper (respectively, lower) approximation operator by using ∗ (respectively, →). Then, She [41] characterized these approximation operators by an axiomatic set. Bao [24] and Wang [42] further characterized them by single axiom. Wang [48] gave a comparative study on variable precision L-fuzzy rough sets. Based on L-fuzzy coverings, Li [49] defined and characterized four L-fuzzy approximation operators. Based on L-fuzzy neighborhood systems, Zhao [45,46,47] proposed a pair of L-fuzzy approximation operators and discussed their axiomatic characterizations and the generated L-fuzzy topology. Song [50] studied the lattice structure induced by the approximation operators.
- ⋄
- Let be a complete co-residuated lattice. Based on L-fuzzy relations, Qiao [38] defined and characterized an L-fuzzy lower approximation operator by using ⊙. He [37] also introduced a granular variable precision L-fuzzy upper approximation operator by using ⊙. Based on distance functions, Oh [51] investigated an L-fuzzy upper approximation operator and discussed the related L-fuzzy topology.
2. Preliminaries
3. -Fuzzy Upper Approximation Operator via ⇝: The Constructive Approach
⇝ | 0 | a | b | 1 |
0 | 0 | a | b | 1 |
a | 0 | 0 | b | b |
b | 0 | a | 0 | a |
1 | 0 | 0 | 0 | 0 |
x | y | |
x | 1 | a |
y | 0 | a |
x | y | |
0 | b | |
a | 0 |
4. Axiomatic Characterization on -Fuzzy Upper Approximation Operators: By Axiomatic Set
5. Axiomatic Characterization on L-Fuzzy Upper Approximation Operators: By Single Axiom
5.1. The Case of One L-Fuzzy Relation
5.2. The Case of Composition of Two L-Fuzzy Relations
5.3. The Case of Composition of Three L-Fuzzy Relations
6. Concluding Remarks
Author Contributions
Funding
Conflicts of Interest
References
- Pawlak, Z. Rough Set. Int. J. Comput. Inf. Sci. 1982, 11, 341–356. [Google Scholar] [CrossRef]
- Pawlak, Z. Rough Set: Theoretical Aspects of Reasoning About Data; Kluwer Academic Publishers: Boston, MA, USA, 1991. [Google Scholar]
- Bonikowski, Z.; Bryniarski, E.; Wybraniec-Skardowska, U. Extensions and intentions in the rough set theory. Inf. Sci. 1998, 107, 149–167. [Google Scholar] [CrossRef]
- Dai, J.H.; Gao, S.C.; Zheng, G.J. Generalized rough set models determined by multiple neighborhoods generated from a similarity relation. Soft Comput. 2018, 22, 2081–2094. [Google Scholar] [CrossRef]
- Greco, S.; Matarazzo, B.; Slowinski, R. Rough approximation by dominance relations. Int. J. Intell. Syst. 2002, 17, 153–171. [Google Scholar] [CrossRef]
- Jin, Q.; Li, L.Q.; Ma, Z.M.; Yao, B.X. A note on the relationships between generalized rough sets and topologies. Int. J. Approx. Reason. 2021, 130, 292. [Google Scholar] [CrossRef]
- Kondo, M. On the structure of generalized rough sets. Inf. Sci. 2005, 176, 589–600. [Google Scholar] [CrossRef]
- Lin, T.Y. Neighborhood Systems: A Qualitative Theory for Fuzzy and Rough Sets; University of California: Berkeley, CA, USA, 2007; p. 94720. [Google Scholar]
- Liu, G.L.; Hua, Z.; Zou, J.Y. Relations arising from coverings and their topological structures. Int. J. Approx. Reason. 2017, 80, 348–358. [Google Scholar] [CrossRef]
- Ma, M.H.; Chakraborty, M.K. Covering-based rough sets and modal logics. Part I. Int. J. Approx. Reason. 2016, 77, 55–65. [Google Scholar] [CrossRef]
- Skowron, A.; Stepaniuk, J. Tolerance approximation spaces. Fundam. Inform. 1996, 27, 245–253. [Google Scholar] [CrossRef]
- Syau, Y.R.; Lin, E.B. Neighborhood systems and covering approximate spaces. Knowl-Based Syst. 2014, 66, 61–67. [Google Scholar] [CrossRef]
- Yang, X.B.; Zhang, M.; Dou, H.L.; Yang, J.Y. Neighborhood systems-based rough sets in incomplete information system. Knowl. Based Syst. 2011, 24, 858–867. [Google Scholar] [CrossRef]
- Yao, Y.Y. Neighborhood systems and approximate retrieval. Inf. Sci. 2006, 176, 3431–3452. [Google Scholar] [CrossRef]
- Yao, Y.Y. Constructive and algebraic methods of the theory of rough sets. Inf. Sci. 1998, 109, 21–47. [Google Scholar] [CrossRef]
- Yao, Y.Y.; Yao, B.X. Covering based rough set approximations. Inf. Sci. 2012, 200, 91–107. [Google Scholar] [CrossRef]
- Zhu, W. Relationship between generalized rough sets based on binary relation and covering. Inf. Sci. 2009, 179, 210–225. [Google Scholar] [CrossRef]
- Lang, G.M. A general conflict analysis model based on three-way decision. Int. J. Mach. Learn. Cybern. 2020, 11, 1083–1094. [Google Scholar] [CrossRef]
- Pan, R.; Wang, X.; Yi, C.; Zhang, Z.; Fan, Y.; Bao, W. Multi-objective optimization method for thresholds learning and neighborhood computing in a neighborhood based decision-theoretic rough set model. Neurocomputing 2017, 266, 619–630. [Google Scholar] [CrossRef]
- Shao, S.T.; Zhang, X.H. Multiobjective programming approaches to obtain the priority vectors under uncertain probabilistic dual hesitant fuzzy preference environment. Int. J. Comput. Intell. Syst. 2021, 14, 1189–1207. [Google Scholar] [CrossRef]
- Zhang, K.; Zhan, J.M.; Yao, Y.Y. TOPSIS method based on a fuzzy covering approximation space: An application to biological nano-materials selection. Inf. Sci. 2019, 502, 297–329. [Google Scholar] [CrossRef]
- Zhao, F.F.; Li, L.Q. Axiomatization on generalized neighborhood system-based rough sets. Soft Comput. 2018, 22, 6099–6110. [Google Scholar] [CrossRef]
- Grigorenko, O.; Minana, J.J.; Sostak, A.; Valero, O. On t-Conorm Based Fuzzy (Pseudo)metrics. Axioms 2020, 9, 78. [Google Scholar] [CrossRef]
- Bao, Y.L.; Yang, H.L.; She, Y.H. Using one axiom to characterize L-fuzzy rough approximation operators based on residuated lattices. Fuzzy Sets Syst. 2018, 336, 87–115. [Google Scholar] [CrossRef]
- D’eer, L.; Cornelis, C. A comprehensive study of fuzzy covering-based rough set models: Definitions, properties and interrelationships. Fuzzy Sets Syst. 2018, 336, 1–26. [Google Scholar]
- D’eer, L.; Cornelis, C.; Godo, L. Fuzzy neighborhood operators based on fuzzy coverings. Fuzzy Sets Syst. 2017, 312, 17–35. [Google Scholar]
- Han, S.E.; Kim, I.S.; Šostak, A. On approximate-type systems generated by L-relations. Inf. Sci. 2014, 281, 8–21. [Google Scholar] [CrossRef]
- Li, L.Q.; Jin, Q.; Yao, B.X. A rough set model based on fuzzifying neighborhood systems. Soft Comput. 2020, 24, 6085–6099. [Google Scholar] [CrossRef]
- Li, L.Q.; Yao, B.X.; Zhan, J.M.; Jin, Q. L-fuzzifying approximation operators derived from general L-fuzzifying neighborhood systems. Int. J. Mach. Learn. Cybern. 2021, 12, 1343–1367. [Google Scholar] [CrossRef]
- Li, T.J.; Leung, Y.; Zhang, W.X. Generalized Fuzzy Rough Approximation Operators Based on Fuzzy Coverings. Int. J. Approx. Reason. 2009, 48, 836–856. [Google Scholar] [CrossRef] [Green Version]
- Liu, G.L. Using one axiom to characterize rough set and fuzzy rough set approximations. Int. J. Approx. Reason. 2017, 80, 348–358. [Google Scholar] [CrossRef]
- Mi, J.S.; Leung, Y.; Zhao, H.Y.; Feng, T. Generalized fuzzy rough sets determined by a triangular norm. Inf. Sci. 2008, 178, 3203–3213. [Google Scholar] [CrossRef]
- Močkoř, J. Functors among Relational Variants of Categories Related to L-Fuzzy Partitions, L-Fuzzy Pretopological Spaces and L-Fuzzy Closure Spaces. Axioms 2020, 9, 63. [Google Scholar] [CrossRef]
- Morsi, N.N.; Yakout, M.M. Axiomatics for fuzzy rough sets. Fuzzy Sets Syst. 1998, 100, 327–342. [Google Scholar] [CrossRef]
- Pang, B.; Mi, J.S.; Yao, W. L-fuzzy rough approximation operators via three new types of L-fuzzy relations. Soft Comput. 2019, 23, 11433–11446. [Google Scholar] [CrossRef]
- Pang, B.; Mi, J.S. Using single axioms to characterize L-rough approximate operators with respect to various types of L-relations. Int. J. Mach. Learn. Cybernet. 2020, 11, 1061–1082. [Google Scholar] [CrossRef]
- Qiao, J.S.; Hu, B.Q. Granular variable recision L-fuzzy rough sets based on residuated lattices. Fuzzy Sets Syst. 2018, 336, 148–166. [Google Scholar] [CrossRef]
- Qiao, J.S.; Hu, B.Q. On (⊙, ∗)-fuzzy rough sets based on residuated and co-residuated lattices. Fuzzy Sets Syst. 2018, 336, 54–86. [Google Scholar] [CrossRef]
- Radzikowska, A.M.; Kerre, E.E. A comparative study of fuzzy rough sets. Fuzzy Sets Syst. 2002, 126, 137–155. [Google Scholar] [CrossRef]
- Radzikowska, A.M.; Kerre, E.E. Fuzzy Rough Sets Based on Residuated Lattices, Transactions on Rough Sets II. LNCS 2004, 3135, 278–296. [Google Scholar]
- She, Y.H.; Wang, G.J. An axiomatic approach of fuzzy rough sets based on residuated lattices. Comput. Math. Appl. 2009, 58, 189–201. [Google Scholar] [CrossRef] [Green Version]
- Wang, C.Y.; Zhang, X.G.; Wu, Y.H. New results on single axiom for L-fuzzy rough approximation operators. Fuzzy Sets Syst. 2020, 380, 131–149. [Google Scholar] [CrossRef]
- Wu, W.Z.; Li, T.J.; Gu, S.M. Using one axiom to characterize fuzzy rough approximation operators determined by a fuzzy implication operator. Fundam. Inform. 2015, 142, 87–104. [Google Scholar] [CrossRef]
- Wu, W.Z.; Zhang, W.X. Constructive and axiomatic approaches of fuzzy approximation operators. Inf. Sci. 2004, 159, 233–254. [Google Scholar] [CrossRef]
- Zhao, F.F.; Jin, Q.; Li, L.Q. The axiomatic characterizations on L-generalized fuzzy neighborhood system-based approximation operators. Int. J. Gen. Syst. 2018, 42, 155–173. [Google Scholar] [CrossRef]
- Zhao, F.F.; Li, L.Q.; Sun, S.B.; Jin, Q. Rough approximation operators based on quantale-valued fuzzy generalized neighborhood systems. Iran. J. Fuzzy Syst. 2019, 16, 53–63. [Google Scholar]
- Zhao, F.F.; Shi, F.G. L-fuzzy generalized neighborhood system operator-based L-fuzzy approximation operators. Int. J. Gen. Syst. 2021, 50, 458–484. [Google Scholar] [CrossRef]
- Wang, C.Y. A comparative study of variable precision fuzzy rough sets based on residuated lattices. Fuzzy Sets Syst. 2019, 373, 94–105. [Google Scholar] [CrossRef]
- Li, L.Q.; Jin, Q.; Hu, K.; Zhao, F.F. The axiomatic characterizations on L-fuzzy covering-based approximation operators. Int. J. Gen. Syst. 2017, 46, 332–353. [Google Scholar] [CrossRef]
- Song, Q.L.; Zhao, H.; Zhang, J.J.; Ramadan, A.A.; Zhang, H.Y.; Chen, G.X. The Lattice Structures of Approximation Operators Based on L-Fuzzy Generalized Neighborhood Systems. Complexity 2021. [Google Scholar] [CrossRef]
- Oh, J.M.; Kim, C.Y. Distance functions, upper approximation operators and Alexandrov fuzzy topologies. J. Intell. Fuzzy Syst. 2021. [Google Scholar] [CrossRef]
- Wu, W.Z.; Leung, Y.; Shao, M.W. Generalized fuzzy rough approximation operators determined by fuzzy implicators. Int. J. Approx. Reason. 2013, 54, 1388–1409. [Google Scholar] [CrossRef]
- Fang, B.W.; Hu, B.Q. Granular fuzzy rough sets based on fuzzy implicators and coimplicators. Fuzzy Sets Syst. 2019, 359, 112–139. [Google Scholar] [CrossRef]
- Orłowska, E.; Radzikowska, A.M. Double residuated lattices and their applications. In RelMiCS 2001: Relational Methods in Computer Science, Proceedings of the International Conference on Relational Methods in Computer Science, Rotterdam, The Netherlands, 30 May–3 June 2001; de Swart, H., Ed.; Springer: Berlin/Heidelberg, Germany, 2002; Volume 2561, pp. 171–189. [Google Scholar]
- Baczyński, M.; Jayaram, B. Fuzzy Implications; Springer: Berlin, Germany, 2008. [Google Scholar]
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Jin, Q.; Li, L. L-Fuzzy Rough Approximation Operators Based on Co-Implication and Their (Single) Axiomatic Characterizations. Axioms 2021, 10, 134. https://doi.org/10.3390/axioms10030134
Jin Q, Li L. L-Fuzzy Rough Approximation Operators Based on Co-Implication and Their (Single) Axiomatic Characterizations. Axioms. 2021; 10(3):134. https://doi.org/10.3390/axioms10030134
Chicago/Turabian StyleJin, Qiu, and Lingqiang Li. 2021. "L-Fuzzy Rough Approximation Operators Based on Co-Implication and Their (Single) Axiomatic Characterizations" Axioms 10, no. 3: 134. https://doi.org/10.3390/axioms10030134
APA StyleJin, Q., & Li, L. (2021). L-Fuzzy Rough Approximation Operators Based on Co-Implication and Their (Single) Axiomatic Characterizations. Axioms, 10(3), 134. https://doi.org/10.3390/axioms10030134