Computer Science ›› 2019, Vol. 46 ›› Issue (1): 45-50.doi: 10.11896/j.issn.1002-137X.2019.01.007

• CCDM2018 • Previous Articles     Next Articles

Uncertainty Measure of Rough Fuzzy Sets in Hierarchical Granular Structure

YANG Jie1,2, WANG Guo-yin1, ZHANG Qing-hua1, FENG Lin3   

  1. (Chongqing Key Laboratory of Computational Intelligence,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)1
    (School of Physics and Electronic,Zunyi Normal University,Zunyi,Guizhou 563002,China)2
    (School of Computer Science,Sichuan Normal University,Chengdu 610000,China)3
  • Received:2018-06-10 Online:2019-01-15 Published:2019-02-25

Abstract: There has been a consensus that the uncertainty of Pawlak’s rough sets model is rooted in the objects contained in the boundary region of the target concept,while the uncertainty of rough fuzzy sets results from three regions,because the objects in the positive or negative regions are probably uncertain.Moreover,in rough fuzzy sets model,a fuzzy concept can be characterized by different rough approximation spaces in a hierarchical granular structure,so how will the uncertainty of a fuzzy concept change with granularity? This paper firstly proposed a fuzziness-based uncertainty measure,analyzed the rough fuzzy set model through the average fuzzy sets and drew a conclusion,that is the uncertainty measure for rough fuzzy sets is also suitable for probabilistic rough sets.Based on the fuzziness-based uncertainty measure,this paper revealed the change rules oftheir uncertainty of rough fuzzy sets in a hierarchical granular structure.Then,it discussed the uncertainties of the three regions (positive region,boundary region and negative region) and revealed the change rules of their uncertainty in a hierarchical granular structure.Finally,experimental results demonstrate the effectiveness of the proposed uncertainty measure theory.

Key words: Fuzziness, Hierarchical granular structure, Rough fuzzy sets, Uncertainty measure

CLC Number: 

  • TP311
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