Granular computing and attribute reduction based on a new discernibility function

被引:0
作者
Lin Y. [1 ]
Shuo Y. [2 ]
机构
[1] College of Computer and Information Engineering, Henan Normal University, Xinxiang
[2] School of Computer and Information Technology, Beijing Jiaotong University, Beijing
来源
International Journal of Simulation: Systems, Science and Technology | 2016年 / 17卷 / 33期
关键词
Conjunctive normal form; Discernibility function; Discernibility relation; Discernibility subset; Disjunctive normal form; Granular computing;
D O I
10.5013/IJSSST.a.17.33.24
中图分类号
学科分类号
摘要
This paper first discusses the method of attribute reduction to determine the discernibility matrix and the discernibility function, which lead to some questions being asked. To find the answers, a new discernibility function is introduced based on information systems and a logical formula defined in the information system. Because each formula produces a granule, the new discernibility function also corresponds to a granule viewed as the semantics. Formulas and granules make it possible to connect the discernibility function with granular computing, which is a current topic of data processing in information science. It sets the stage for research on the new discernibility function using a granular computing method. Accordingly, a conclusion is reached which shows the granule produced by the new discernibility function is equal to the union of all discernibility relations generated by the attributes. Some theorems are proved based on the conclusion, which are answers to the questions. © 2016, UK Simulation Society. All rights reserved.
引用
收藏
页码:24.1 / 24.10
相关论文
共 50 条
[21]   Rough decision rules extraction and reduction based on granular computing [J].
Yan H.-C. ;
Zhang F. ;
Liu B.-X. .
Tongxin Xuebao/Journal on Communications, 2016, 37 :30-35
[22]   Quantitative/qualitative region-change uncertainty/certainty in attribute reduction: Comparative region-change analyses based on granular computing [J].
Zhang, Xianyong ;
Miao, Duoqian .
INFORMATION SCIENCES, 2016, 334 :174-204
[23]   Application of granular computing in knowledge reduction [J].
Wei, Lai ;
Miao, Duoqian .
ROUGH SETS AND KNOWLEDGE TECHNOLOGY, PROCEEDINGS, 2006, 4062 :357-362
[24]   A New Granular Computing Model Based on Algebraic Structure [J].
Chen Linshu ;
Wang Jiayang ;
Wang Weicheng ;
Li Li .
CHINESE JOURNAL OF ELECTRONICS, 2019, 28 (01) :136-142
[25]   A New Granular Computing Model Based on Algebraic Structure [J].
CHEN Linshu ;
WANG Jiayang ;
WANG Weicheng ;
LI Li .
ChineseJournalofElectronics, 2019, 28 (01) :136-142
[26]   Knowledge Reduction Based on Granular Computing from Decision Information Systems [J].
Sun, Lin ;
Xu, Jiucheng ;
Li, Shuangqun .
ROUGH SET AND KNOWLEDGE TECHNOLOGY (RSKT), 2010, 6401 :46-53
[27]   A novel attribute reduction algorithm based on granular sequential three-way decision [J].
Chen, Yuliang ;
Cheng, Yunlong ;
Luo, Binbin ;
Shao, Yabin ;
Zhao, Mingfu ;
Zhang, Qinghua .
INFORMATION SCIENCES, 2025, 694
[28]   A granular computing view on function approximation [J].
Zeng, Xiao-Jun ;
Keane, John A. .
2006 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING, 2006, :232-+
[29]   Granular Computing and Knowledge Reduction in Formal Contexts [J].
Wu, Wei-Zhi ;
Leung, Yee ;
Mi, Ju-Sheng .
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2009, 21 (10) :1461-1474
[30]   Approximate reasoning based on granular computing in granular logic [J].
Liu, Q ;
Liu, Q .
2002 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-4, PROCEEDINGS, 2002, :1258-1262