Variable Rough Set Model and Its Knowledge Reduction for Incomplete and Fuzzy Decision Information Systems
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Variable Rough Set Model and Its Knowledge Reduction for Incomplete and Fuzzy Decision Information Systems

Authors: Da-kuan Wei, Xian-zhong Zhou, Dong-jun Xin, Zhi-wei Chen

Abstract:

The information systems with incomplete attribute values and fuzzy decisions commonly exist in practical problems. On the base of the notion of variable precision rough set model for incomplete information system and the rough set model for incomplete and fuzzy decision information system, the variable rough set model for incomplete and fuzzy decision information system is constructed, which is the generalization of the variable precision rough set model for incomplete information system and that of rough set model for incomplete and fuzzy decision information system. The knowledge reduction and heuristic algorithm, built on the method and theory of precision reduction, are proposed.

Keywords: Rough set, Incomplete and fuzzy decision information system, Limited valued tolerance relation, Knowledge reduction, Variable rough set model

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074992

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References:


[1] M. Kryszkiewicz. "Rough set approach to incomplete information systems," Information Sciences, vol. 112, pp. 39-49, 1998.
[2] M. Kryszkiewicz. "Rules in incomplete information systems," Information Sciences, vol. 113, pp. 271-292, 1999.
[3] R. Slowinski and D. Vanderpooten. "A Generalized Definition of Rough Approximation Based on Similarity," IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGIERING, vol. 12. pp. 331-336, March/April 2000.
[4] G. Y. Wang. Rough Set Theory and Knowledge Acquisition. Xi'an: Xi'an JiaoTong University Press, 2001.
[5] G. Y. Wang. "Extension of Rough Set under Incomplete Information systems," Journal of Computer Research and Development, vol.39, no. 10, pp. 1238-1243, Oct. 2002.
[6] W. Z. Wu, J. S. Mi and W. X. Zhang. "A New Rough Set Approach to Knowledge Discovery in Incomplete Information System," Proceedings of the Second International Conference on Machine Learning and Cybernetics, Xi-an, pp. 1713-1718, 2-5 November 2003.
[7] B. Huang, D. K. Wei and X. Z. Zhou. "An algorithm for Maximum Distribution Reductions and Maximum Distribution Rules in Information system," Computer science, vol. 31, no. 10A, pp. 80-83, 2004.
[8] B. Huang and X. Z. Zhou. "Extension of Rough Set Model Based on Connection Degree under Incomplete Information System," Systems Engineering --Theory and Practice, no. 1, pp. 88-92, 2004.
[9] D. K. Wei, X. Z. Zhou and Y. G. Zhu. "Knowledge Reduction in Incomplete Information System Based on Improved-Tolerance Relation," Computer Science, vol. 32, no. 8.A, pp. 53-56, 2005.
[10] D. K. We and X. Z. Zhou. "Rough Set Model in Incomplete and Fuzzy Decision Information System Based on Improved-Tolerance Relation," 2005 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTERING, Tsinghua University, China, pp. 278~283, July 2005.
[11] D. K. Wei, B. Huang and X. Z Zhou. "Rough Set Model and Knowledge Reduction in Incomplete and Fuzzy Objective Information system," Computer Engineering, no.7, 2006. (to be published)
[12] D. K. Wei, and X. Z Zhou. "A Rough Set Approach to Incomplete and fuzzy decision information system," 2006 IEEE the 6th World Congress on Intelligent Control Automation, Dalian, China, July 21-23, 2005. (to be published)
[13] H.Y. Zhang, and J.Y. Liang. "Variable Precision Rough Set Model and a Knowledge Reduction Algorithm for Incomplete Information System," Computer Science, vol.30, no. 4, pp: 153-155, 2003.