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Uncertainty measurement for interval set decision informationtables based on conditional information entropy(PDF)


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Uncertainty measurement for interval set decision informationtables based on conditional information entropy
Zhang YimengJia XiuyiTang Zhenmin
School of Computer Science and Engineering,Nanjing University of Science andTechnology,Nanjing 210094,China
rough set theory uncertainty measurement interval set decision tables approximation sets granular computing
This paper aims at studying uncertainty measures for interval set decision information tables based on conditional information entropy. A binary similarity relation,called δ-interval similarity relation in an interval set decision table is proposed to depict the relationships of objects. Based on this relation,the extended uncertainty measures from Pawlak rough set model,namely,approximate accuracy and approximate roughness,are defined in interval set decision information tables. According to the analysis of δ-interval approximate accuracy and δ-interval approximate roughness,they are not sensitive to the variation of granular structure. A new uncertainty measure called δ-interval decision conditional entropy is proposed by combining with conditional information entropy in interval set decision tables. The associated properties of δ-interval approximate accuracy,δ-interval approximate roughness and δ-interval decision conditional entropy are analyzed and proved. Through an actual example,the proposed δ-interval decision conditional entropy can measure the uncertainty of interval set decision tables effectively and accurately.


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Last Update: 2019-09-30