Properties of approximation operators over 1-neighborhood systems from the perspective of special granules

Abstract As generalizations of Pawlak-neighborhood systems, 1-neighborhood systems with symmetry or transitivity are closely related to both partition spaces and covering spaces. In this article, we analyze the properties of a single covering-based approximation operator on symmetric or transitive 1-neighborhood systems. We also investigate the relationships between different covering-based approximation operators on them. Theoretically, we illuminate some necessary and sufficient conditions for 1-neighborhood systems being symmetric, transitive, or partitions with one or two approximation operators. To reduce potential computation complexity owing to these equivalent characterizations, objects dealt by approximation operators in this work are three particular kinds of granules, namely, points of universes, elements of 1-neighborhood systems, and cores of 1-neighborhood systems. As experimental results indicate, this study outdoes some related works in terms of computational efficiency, establishing the advantages of computing on these granules. Furthermore, our research has resulted in a solution to a problem posed by Yun et al. (Axiomatization and conditions for neighborhoods in a covering to form a partition. Information Sciences 181(2011)1735–1740).

[1]  W. Zakowski APPROXIMATIONS IN THE SPACE (U,π) , 1983 .

[2]  Jianming Zhan,et al.  Covering based multigranulation (I, T)-fuzzy rough set models and applications in multi-attribute group decision-making , 2019, Inf. Sci..

[3]  Zheng Pei,et al.  Generalized rough sets based on reflexive and transitive relations , 2008, Inf. Sci..

[4]  Wei-Jiang Liu,et al.  Topological space properties of rough sets , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

[5]  Guilong Liu,et al.  Rough set theory based on two universal sets and its applications , 2010, Knowl. Based Syst..

[6]  Yiyu Yao,et al.  Covering based rough set approximations , 2012, Inf. Sci..

[7]  Li Zhang,et al.  Novel classes of fuzzy soft $$\beta $$β-coverings-based fuzzy rough sets with applications to multi-criteria fuzzy group decision making , 2018, Soft Comput..

[8]  Guilong Liu Special types of coverings and axiomatization of rough sets based on partial orders , 2015, Knowl. Based Syst..

[9]  J. A. Pomykala,et al.  The stone algebra of rough sets , 1988 .

[10]  William Zhu,et al.  The algebraic structures of generalized rough set theory , 2008, Inf. Sci..

[11]  Ewa Orlowska,et al.  Semantic Analysis of Inductive Reasoning , 1986, Theor. Comput. Sci..

[12]  Andrzej Skowron,et al.  Rough sets and Boolean reasoning , 2007, Inf. Sci..

[13]  Ming Zhang,et al.  Neighborhood systems-based rough sets in incomplete information system , 2011, Knowl. Based Syst..

[14]  Urszula Wybraniec-Skardowska,et al.  Extensions and Intentions in the Ruogh Set Theory , 1998, Inf. Sci..

[15]  Washek F. Pfeffer,et al.  Some properties of the Sorgenfrey line and related spaces. , 1979 .

[16]  Wei-Zhi Wu,et al.  Neighborhood operator systems and approximations , 2002, Inf. Sci..

[17]  Gianpiero Cattaneo,et al.  Abstract Approximation Spaces for Rough Theories , 2008 .

[18]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[19]  Xiaole Bai,et al.  Axiomatization and conditions for neighborhoods in a covering to form a partition , 2011, Inf. Sci..

[20]  T. Medhat,et al.  Rough set theory for topological spaces , 2005, Int. J. Approx. Reason..

[21]  Yiyu Yao,et al.  Relational Interpretations of Neigborhood Operators and Rough Set Approximation Operators , 1998, Inf. Sci..

[22]  Jie Liu,et al.  The relationship between coverings and tolerance relations , 2010, Int. J. Granul. Comput. Rough Sets Intell. Syst..

[23]  Chris Cornelis,et al.  Neighborhood operators for covering-based rough sets , 2016, Inf. Sci..

[24]  Michiro Kondo,et al.  On the structure of generalized rough sets , 2006, Inf. Sci..

[25]  T. Y. Lin,et al.  Neighborhood systems and relational databases , 1988, CSC '88.

[26]  A. Wasilewska,et al.  Conditional knowledge representation systems -model for an implementation , 1989 .

[27]  Xiaole Bai,et al.  A study of rough sets based on 1-neighborhood systems , 2013, Inf. Sci..

[28]  William Zhu,et al.  Topological approaches to covering rough sets , 2007, Inf. Sci..

[29]  Fei-Yue Wang,et al.  Reduction and axiomization of covering generalized rough sets , 2003, Inf. Sci..

[30]  Andrzej Skowron,et al.  Rough sets: Some extensions , 2007, Inf. Sci..

[31]  Daniel Vanderpooten,et al.  A Generalized Definition of Rough Approximations Based on Similarity , 2000, IEEE Trans. Knowl. Data Eng..

[32]  Yiyu Yao,et al.  Rough set models in multigranulation spaces , 2016, Inf. Sci..

[33]  Yiyu Yao,et al.  MGRS: A multi-granulation rough set , 2010, Inf. Sci..

[34]  Andrzej Skowron,et al.  Tolerance Approximation Spaces , 1996, Fundam. Informaticae.

[35]  Yan Gao,et al.  On Covering Rough Sets , 2007, RSKT.

[36]  Yiyu Yao,et al.  Generalization of Rough Sets using Modal Logics , 1996, Intell. Autom. Soft Comput..