Neighborhood Systems and Variable Precision Generalized Rough Sets

In this paper, we present the connection between the concepts of Variable Precision Generalized Rough Set model (VPGRS-model) and Neighborhood Systems through binary relations. We provide characterizations of lower and upper approximations for VPGRS-model by introducing minimal neighborhood systems. Furthermore, we explore generalizations by investigating variable parameters which are limited by variable precision. We also prove some properties of lower and upper approximations for VPGRS-model.

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

[2]  Mark Steedman,et al.  In handbook of logic and language , 1997 .

[3]  Churn-Jung Liau,et al.  An Overview of Rough Set Semantics for Modal and Quantifier Logics , 2000, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[4]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[5]  Jerzy W. Grzymala-Busse,et al.  Generalized probabilistic approximations of incomplete data , 2014, Int. J. Approx. Reason..

[6]  Tsau Young Lin,et al.  Granular computing for binary relations: clustering and axiomatic granular operators , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[7]  Chen Degang,et al.  Variable precision rough set model based on general relations , 2005 .

[8]  Andrzej Skowron,et al.  Generalized Quantifiers in the Context of Rough Set Semantics , 2015, Fundam. Informaticae.

[9]  Andrzej Skowron,et al.  Inclusion degree with variable-precision model in analyzing inconsistent decision tables , 2017, GRC 2017.

[10]  Dominik Slezak,et al.  Rough Sets and Bayes Factor , 2005, Trans. Rough Sets.

[11]  Jouni Järvinen Pawlak's Information Systems in Terms of Galois Connections and Functional Dependencies , 2007, Fundam. Informaticae.

[12]  Tsau Young Lin,et al.  Granular Mathematics foundation and current state , 2011, 2011 IEEE International Conference on Granular Computing.

[13]  Jouni Järvinen Properties of Rough Approximations , 2005, J. Adv. Comput. Intell. Intell. Informatics.

[14]  Stanley Peters,et al.  Quantifiers in language and logic , 2006 .

[15]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

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

[17]  Zengtai Gong,et al.  Variable Precision Rough Set Model for Incomplete Information Systems and Its Beta-Reducts , 2013 .

[18]  Wu Yue Variable precision rough set model based on general relation , 2008 .

[19]  Theresa Beaubouef,et al.  Rough Sets , 2019, Lecture Notes in Computer Science.

[20]  Duen-Ren Liu,et al.  A Uniform Framework for Rough Approximations Based on Generalized Quantifiers , 2015, Trans. Rough Sets.

[21]  Marzena Kryszkiewicz,et al.  Rough Set Approach to Incomplete Information Systems , 1998, Inf. Sci..

[22]  Zoltán Csajbók,et al.  Approximation of Sets Based on Partial Covering , 2011, Trans. Rough Sets.

[23]  Churn-Jung Liau,et al.  Modal Reasoning and Rough Set Theory , 1998, AIMSA.

[24]  M. Day,et al.  Convergence, closure and neighborhoods , 1944 .

[25]  Dominik Slezak,et al.  The investigation of the Bayesian rough set model , 2005, Int. J. Approx. Reason..

[26]  Dag Westerståhl,et al.  Generalized Quantifiers in Linguistics and Logic , 1997, Handbook of Logic and Language.

[27]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[28]  Jerzy W. Grzymala-Busse,et al.  Consistency of incomplete data , 2015, Inf. Sci..