The lattice and matroid representations of definable sets in generalized rough sets based on relations

Abstract The definable set is a core concept in rough set theory. It plays an important role in the characterizations of rough sets. In this paper, we study the lattice and matroid representations of definable sets in generalized rough sets based on relations. First, we propose the lower definable lattice, consisting of all lower definable sets with set inclusion order. Then we give some conditions under which the lower definable lattice is distributive (or geometric, or Boolean). Furthermore, we discuss the relationship between distributive lattices and the lower definable lattices in generalized approximation spaces based on reflexive and transitive relations. On the one hand, we show that the lower definable lattice in a generalized approximation space based on reflexive and transitive relation is distributive. On the other hand, we obtain the result that a distributive lattice can induce a lower definable lattice in a generalized approximation space based on a reflexive and transitive relation. Finally, we investigate the combination of generalized rough sets and matroids in terms of the lower definable lattice. We show that if a lower definable lattice is a lattice of closed sets of a matroid, then it must be an open-closed set lattice of a matroid. In addition, we prove that some lower definable lattices are not the lattices of closed sets of matroids. These results of this paper will benefit to our understanding of the relationship between matroids and generalized rough sets based on relations.

[1]  Yee Leung,et al.  Optimal scale selection for multi-scale decision tables , 2013, Int. J. Approx. Reason..

[2]  William Zhu,et al.  Relationship between generalized rough sets based on binary relation and covering , 2009, Inf. Sci..

[3]  Aiping Huang,et al.  Nullity-based matroid of rough sets and its application to attribute reduction , 2014, Inf. Sci..

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

[5]  Jing-Yu Yang,et al.  Test cost sensitive multigranulation rough set: Model and minimal cost selection , 2013, Inf. Sci..

[6]  Qingxin Zhu,et al.  Quantitative analysis for covering-based rough sets through the upper approximation number , 2013, Inf. Sci..

[7]  Davide Ciucci,et al.  Micro and macro models of granular computing induced by the indiscernibility relation , 2017, Inf. Sci..

[8]  Qingxin Zhu,et al.  Matroidal structure of rough sets and its characterization to attribute reduction , 2012, Knowl. Based Syst..

[9]  Hiroshi Tanaka,et al.  Algebraic Specification of Empirical Inductive Learning Methods based on Rough Sets and Matroid Theory , 1994, AISMC.

[10]  William Zhu,et al.  Generalized rough sets based on relations , 2007, Inf. Sci..

[11]  Sanyang Liu,et al.  Matroidal approaches to rough sets via closure operators , 2012, Int. J. Approx. Reason..

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

[13]  Hiroshi Tanaka,et al.  AQ, Rough Sets, and Matroid Theory , 1993, RSKD.

[14]  Daniel S. Yeung,et al.  Rough approximations on a complete completely distributive lattice with applications to generalized rough sets , 2006, Inf. Sci..

[15]  Jiye Liang,et al.  International Journal of Approximate Reasoning an Efficient Rough Feature Selection Algorithm with a Multi-granulation View , 2022 .

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

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

[18]  Huangjian Yi,et al.  Rough sets and matroids from a lattice-theoretic viewpoint , 2016, Inf. Sci..

[19]  Hong Wang,et al.  The approximation number function and the characterization of covering approximation space , 2015, Inf. Sci..

[20]  Qingguo Li,et al.  Reduction about approximation spaces of covering generalized rough sets , 2010, Int. J. Approx. Reason..

[21]  Fei-Yue Wang,et al.  Properties of the Fourth Type of Covering-Based Rough Sets , 2006, 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06).

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

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

[24]  Li Zheng,et al.  Topology vs generalized rough sets , 2011, Int. J. Approx. Reason..

[25]  Jesús Medina,et al.  Multi-adjoint property-oriented and object-oriented concept lattices , 2012, Inf. Sci..

[26]  Deng Han-yuan The Properties of Rough Sets , 2007 .

[27]  Yiyu Yao,et al.  Constructive and Algebraic Methods of the Theory of Rough Sets , 1998, Inf. Sci..

[28]  Jiye Liang,et al.  Multigranulation rough sets: From partition to covering , 2013, Inf. Sci..

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

[30]  Davide Ciucci,et al.  The granular partition lattice of an information table , 2016, Inf. Sci..

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

[32]  Xiaohong Zhang,et al.  Constructive methods of rough approximation operators and multigranulation rough sets , 2016, Knowl. Based Syst..

[33]  Zhengang Zhao,et al.  On some types of covering rough sets from topological points of view , 2016, Int. J. Approx. Reason..

[34]  Yanhong She,et al.  On the structure of the multigranulation rough set model , 2012, Knowl. Based Syst..

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

[36]  Marcin Wolski Complete Orders, Categories and Lattices of Approximations , 2006, Fundam. Informaticae.

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

[38]  Kai Zhu,et al.  The relationship among three types of rough approximation pairs , 2014, Knowl. Based Syst..

[39]  William Zhu,et al.  Rough matroids based on relations , 2013, Inf. Sci..