Reduction of Neighborhood-Based Generalized Rough Sets

Rough set theory is a powerful tool for dealing with uncertainty, granularity, and incompleteness of knowledge in information systems. This paper discusses five types of existing neighborhood-based generalized rough sets. The concepts of minimal neighborhood description and maximal neighborhood description of an element are defined, and by means of the two concepts, the properties and structures of the third and the fourth types of neighborhood-based rough sets are deeply explored. Furthermore, we systematically study the covering reduction of the third and the fourth types of neighborhood-based rough sets in terms of the two concepts. Finally, two open problems proposed by Yun et al. (2011) are solved.

[1]  Tefko Saracevic,et al.  Information science: What is it? , 1968 .

[2]  Daniel S. Yeung,et al.  Approximations and reducts with covering generalized rough sets , 2008, Comput. Math. Appl..

[3]  Fei-Yue Wang,et al.  Covering Based Granular Computing for Conflict Analysis , 2006, ISI.

[4]  Wojciech Rzasa,et al.  Definability of Approximations for a Generalization of the Indiscernibility Relation , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[5]  William Zhu,et al.  On Three Types of Covering-Based Rough Sets , 2014, IEEE Transactions on Knowledge and Data Engineering.

[6]  Sankar K. Pal,et al.  Granular computing, rough entropy and object extraction , 2005, Pattern Recognit. Lett..

[7]  Yee Leung,et al.  Generalized fuzzy rough approximation operators based on fuzzy coverings , 2008, Int. J. Approx. Reason..

[8]  Wen-Xiu Zhang,et al.  Measuring roughness of generalized rough sets induced by a covering , 2007, Fuzzy Sets Syst..

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

[10]  Yiyu Yao,et al.  On Generalizing Rough Set Theory , 2003, RSFDGrC.

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

[12]  Yiyu Yao,et al.  Peculiarity Oriented Multidatabase Mining , 2003, IEEE Trans. Knowl. Data Eng..

[13]  Potential Applications of Granular Computing in Knowledge Discovery and Data Mining , 1999 .

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

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

[16]  Ning Zhong,et al.  Using Rough Sets with Heuristics for Feature Selection , 1999, RSFDGrC.

[17]  Wenbin Li,et al.  Developing Intelligent Applications in Social E-Mail Networks , 2006, RSCTC.

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

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

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

[21]  Xizhao Wang,et al.  On the generalization of fuzzy rough sets , 2005, IEEE Transactions on Fuzzy Systems.

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

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

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

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

[26]  Sadaaki Miyamoto,et al.  Rough Sets and Current Trends in Computing , 2012, Lecture Notes in Computer Science.

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

[28]  Fei-Yue Wang,et al.  Binary Relation Based Rough Sets , 2006, FSKD.

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