Knowledge Reduction of Covering Approximation Space

Covering approximation space is a kind of knowledge representation different from Pawlak's approximation space, and knowledge reduction is the key step in knowledge acquisition. Zhu proposed an absolute reduction of covering approximation space, but it could only reduce absolutely redundant knowledge. In order to reduce relatively redundant knowledge with respect to a decision, the problem of relative reduction is studied in this paper. We find that the rough approximations keep unchanged in the reduced space. In addition, an algorithm for knowledge reduction of covering approximation space is proposed. It can reduce not only absolutely redundant knowledge but also relatively redundant knowledge.

[1]  Sankar K. Pal,et al.  Case generation using rough sets with fuzzy representation , 2004, IEEE Transactions on Knowledge and Data Engineering.

[2]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[3]  Marzena Kryszkiewicz,et al.  Rules in Incomplete Information Systems , 1999, Inf. Sci..

[4]  John N. Mordeson,et al.  Rough set theory applied to (fuzzy) ideal theory , 2001, Fuzzy Sets Syst..

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

[6]  Guoyin Wang,et al.  Extension of rough set under incomplete information systems , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

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

[8]  Fei-Yue Wang,et al.  A New Type of Covering Rough Set , 2006, 2006 3rd International IEEE Conference Intelligent Systems.

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

[10]  Guoyin Wang,et al.  Uncertainty Measure of Covering Generated Rough Set , 2006, 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops.

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

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

[13]  J.W.T. Lee,et al.  On the upper approximations of covering generalized rough sets , 2004, Proceedings of 2004 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.04EX826).

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

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

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

[17]  Guoyin Wang,et al.  A direct approach for incomplete information systems , 2004, SPIE Defense + Commercial Sensing.

[18]  Tong-Jun Li.  Rough Approximation Operators in Covering Approximation Spaces , 2006, RSCTC.

[19]  Guoyin Wang,et al.  Feature Selection in Audiovisual Emotion Recognition Based on Rough Set Theory , 2007, Trans. Rough Sets.

[20]  Guoyin Wang,et al.  Motion-Information-Based Video Retrieval System Using Rough Pre-classification , 2006, Trans. Rough Sets.

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