Int. J. of Granular Computing, Rough Sets and Intelligent Systems   »   2009 Vol.1, No.2

 

 

Title: DRSA and reductions in incomplete fuzzy information system

 

Author: Lihua Wei, Zhenmin Tang, Xibei Yang, Limin Xu

 

Addresses:
School of Computer Science and Technology, Nanjing University of Science and Technology, 210094 Nanjing, P.R. China; College of Information Science & Technology, Drexel University, Philadelphia, PA 19104, USA.
School of Computer Science and Technology, Nanjing University of Science and Technology, 210094 Nanjing, P.R. China.
School of Computer Science and Technology, Nanjing University of Science and Technology, 210094 Nanjing, P.R. China.
Key Laboratory of Electronic Business, Nanjing University of Finance and Economics, 210003 Nanjing, P.R. China

 

Abstract: Although many extended rough set models have been successfully applied into the incomplete information system, most of them do not take the incomplete information system with initial fuzzy data into account. This paper thus presents a general framework for the study of dominance-based rough set approach to the incomplete fuzzy information systems. First, the traditional dominance relation is expanded in the incomplete fuzzy information system by assuming the condition attributes, which are not only positively but also negatively related with classification analysis. We then present the dominance-based rough approximations by the rough fuzzy technique. Finally, we propose two types of knowledge reductions, relative lower and upper approximate reducts, which can be used to induce simplified decision rules from the incomplete fuzzy decision table. We also present the judgement theorems and discernibility functions, which describe how relative lower and upper approximate reducts can be calculated. We employ some numerical examples in this paper to substantiate the conceptual arguments.

 

Keywords: incomplete information systems; fuzzy information systems; dominance relations; rough sets; fuzzy sets; relative reducts; decision rules; fuzzy logic.

 

DOI: 10.1504/IJGCRSIS.2009.028004

 

Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2009 Vol.1, No.2, pp.121 - 136

 

Available online: 27 Aug 2009

 

 

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