Computer Science ›› 2018, Vol. 45 ›› Issue (8): 186-190.doi: 10.11896/j.issn.1002-137X.2018.08.033

• Artificial Intelligence • Previous Articles     Next Articles

Supervised Neighborhood Rough Set

WANG Lin-na1,2, YANG Xin3, YANG Xi-bei4   

  1. School of Electronic and Information Engineering,Sichuan Technology and Business University,Chengdu 611745,China1
    Department of Computer Science,University of Regina,Regina,Saskatchewan S4S 0A2,Canada2
    School of Information Science and Technology,Southwest Jiaotong University,Chengdu 611756,China3
    School of Computer Science and Engineering,Jiangsu University of Science and Technology,Zhenjiang,Jiangsu 212003,China4
  • Received:2017-07-21 Online:2018-08-29 Published:2018-08-29

Abstract: The uncertainty of information can’t be efficiently reduced by traditional neighborhood rough set with single threshold.By considering the existing or predicted category label information of the object,this paper introduced two kinds of thresholds,namely,intra-class and inter-class,and proposed a novel neighborhood granulation methods to construct a rough set model based on supervised neighborhood.This model is the generalized form of conventional neighborhood rough set.Moreover,the theorem of monotonic variation with approximate quality and conditional entropy was presented through analyzing the change rules of neighborhood particlesunder double thresholds.Finally,the performance of the model was demonstrated on four data sets of UCI.The results show that the effect of neighborhood granulation can be improved andthe uncertainty of information can be reduced by adjusting supervised threshold parameters.

Key words: Double thresholds, Neighborhood granulation, Supervised neighborhood, Uncertainty

CLC Number: 

  • TP181
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