Abstract
Based on the theory of rough sets and intuitionistic fuzzy sets, this paper researches attribute reduction in intuitionistic fuzzy decision systems (IFDS). Firstly, we establish an intuitionistic fuzzy rough set model based on the similarity relation. Secondly, the discernibility matrix based on the maximal consistent block is constructed and an algorithm of attribute reduction is designed, which can eliminate the redundant information from the given IFDS. Finally, an illustrative example is employed to show the validity of the algorithm in this paper.
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Feng, Q., Li, R. (2013). Discernibility Matrix Based Attribute Reduction in Intuitionistic Fuzzy Decision Systems. In: Ciucci, D., Inuiguchi, M., Yao, Y., Ślęzak, D., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2013. Lecture Notes in Computer Science(), vol 8170. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41218-9_16
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DOI: https://doi.org/10.1007/978-3-642-41218-9_16
Publisher Name: Springer, Berlin, Heidelberg
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