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Generalization Rough Set Theory 被引量:2

Generalization Rough Set Theory
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摘要 In order to avoid the discretization in the classical rough set theory, a generlization rough set theory is proposed. At first, the degree of general importance of an attribute and attribute subsets are presented. Then, depending on the degree of general importance of attribute, the space distance can be measured with weighted method. At last, a generalization rough set theory based on the general near neighborhood relation is proposed. The proposed theory partitions the universe into the tolerant modules, and forms lower approximation and upper approximation of the set under general near neighborhood relationship, which avoids the discretization in Pawlak's rough set theory. In order to avoid the discretization in the classical rough set theory, a generlization rough set theory is proposed. At first, the degree of general importance of an attribute and attribute subsets are presented. Then, depending on the degree of general importance of attribute, the space distance can be measured with weighted method. At last, a generalization rough set theory based on the general near neighborhood relation is proposed. The proposed theory partitions the universe into the tolerant modules, and forms lower approximation and upper approximation of the set under general near neighborhood relationship, which avoids the discretization in Pawlak's rough set theory.
出处 《Journal of Donghua University(English Edition)》 EI CAS 2008年第6期654-658,共5页 东华大学学报(英文版)
基金 Natural Science Foundation of Jiangsu Province of China ( No.BK2006176) High-Tech Key Laboratory of Jiangsu,China (No.BM2007201)
关键词 generalization rough set theory the degree of general importance general near neighborhood relation 粗糙集理论 社区关系 属性集 空间距离 宇宙理论 加权法 上近似 下近似
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参考文献10

  • 1Yao Y Y.On the Generalizing Rough Set Theory [ C][].Preceedings of Interational Conference on Rough Set Fuzzy Sets Data Mining and Granular Computing.2003
  • 2Han J,Sanchez R.Feature Selection Based on Relative Attribute Dependency An Experi mental Study [ C][].Proceedings of International Conference on Rough Set Fuzzy Set Data Mining and Granular Computing.2005
  • 3Roy A,Pal S K.Fuzzy discretization of feature space for a rough set classifier[].Pattern Recognition.2003
  • 4Daijin K.Data classification based on tolerant rough set[].Pattern Recognition.2001
  • 5Zdzislaw Pawlak.Rough Set Theory[].Künstliche Intelligenz.2001
  • 6Pawlak Z,Grzymala-Busse J,Slowinski R,Ziarko W.Rough Sets[].Communications of the ACM.1995
  • 7Toshiko Wakaki.Rough Set-Aided Feature Selection for Auto-matic Web-Page Classification[].IEEE WI.2004
  • 8Su Chao-Ton,Hsu Jyh-Hwa.An extended Chi2 algorithm for discretization of real value attributes[].IEEE Transactions on Knowledge and Data Engineering.2005
  • 9Dominik Slezak,Wojciech Ziarko.Variable Precision Bayesian Rough set model, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing[].th International Conference RSFDGrC.2003
  • 10Skowron,A.,Stepaniuk,J.Tolerance Approximation Spaces[].Fundamenta Informaticae.1996

同被引文献17

  • 1常春光,汪定伟,胡琨元,陶志.基于粗糙集的案例属性约简技术[J].控制理论与应用,2006,23(6):867-872. 被引量:7
  • 2汪杭军,张广群,方陆明.粗糙集属性约简算法的实现与应用[J].计算机工程与设计,2007,28(4):777-779. 被引量:16
  • 3肖迪,胡寿松.实域粗糙集理论及属性约简[J].自动化学报,2007,33(3):253-258. 被引量:32
  • 4Liu D R, Ke C K. Knowledge support for problem-solving in a pro- duction process: a hybrid of knowledge discovery and case-based rea- soning[J]. Expert Systems with Applictons, 2007,33 ( 1 ) : 147 - 161.
  • 5Xie Gang, Zhang Jinlong, Lai K K, et al. Variable Precision Rough Set For Group Decision-making: An Application E .l ]. International Journal of Approximate Reasoning, 2008, 49(2) :331 -343.
  • 6Banerjee M, Mitra S, Banka H. Evolutinary-rough feature sdection in gent expression Data[J]. IEEE Transaction on Systems, Man, and Cy- bemeticd, Part C: Application and Reviews,2007,37:622 -632.
  • 7Petcu A. A Class of Algorithms for Distributed Constraint Optimiza- tion[M]. ISBN 978 - 1 -58603 -989 -9. 2009.
  • 8Yang M, Yang P. A novel condensing tree structure for rough set feature selection[ J]. Neurocomputing,2008,71 : 1092 - 1100.
  • 9Skowron A, Stepaniuk J. Tolerance approximation spaces [ J ]. Fundamental informaton, 1996,46 : 245 - 253.
  • 10Daijin Kim, Sung-Yang Bang. IRIS clam classification using tolerant rough sets [ J ]. Journal of Advanced Computational Intelligence, 2000,4(5) :327-335.

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