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
[1]PAWLAK Z.Rough sets[J].International Journal of Computer &Information Sciences,1982,11(5):341-356.
[2]LIN T Y.Granular computing:practices,theories,and future directions [M].New York:Springer,2009:4339-4355.
[3]LIN T Y.Granular computing on binary relation I:Date mining and neighborhood systems [J].Rough Sets in Knowledge Discovery,1998(2):165-166.
[4]YAO Y Y.Relational interpretation of neighborhood operators and rough set approximation operators[J].Information Scien-ces,1998,111(198):239-259.
[5]WU W Z,ZHANG W X.Neighborhood operator systems and approximations [J].Information Sciences,2002,144(1-4):201-217.
[6]HU Q H,YU D R,XIE Z X.Neighborhood classifiers[J].Expert Systems with Applications,2008,34(2):866-876.
[7]HU Q H,PEDRYCZ W,YU D R,et al.Selecting discrete and continuous featuresbased on neighborhood decision error minimization [J].IEEE Transactions on Systems,Man,and Cybernetics,Part B (Cybernetics),2010,40(1):137-150.
[8]DUAN J,HU Q H,ZHANG L J,et al.Feature selection formulti-label classification based on neighborhood rough sets[J].Journal of Computer Research and Development,2015,52(1):56-65.
[9]CHEN D G,LI W L,ZHANG X,et al.Evidence-theory-based numerical algorithms of attribute reduction with neighborhood-covering rough sets[J].International Journal of Approximate Reasoning,2014,55(3):908-923.
[10]YANG X B,ZHANG M,DOU H L,et al.Neighborhood Systems-Based Rough Sets in Incomplete Information System[J].Knowledge-Based Systems,2011,24(6):858-867.
[11]YANG X B,CHEN Z H,DOU H L,et al.Neighborhood system based rough set:models and attribute reductions[J].International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems,2012,20(3):399-419.
[12]ZHANG J B,LI T R,RUAN D,et al.Neighborhood rough sets for dynamic data mining [J].International Journal of Intelligent Systems,2012,27(4):317-342.
[13]CHEN H M,LI T R,CAI Y,et al.Parallel attribute reduction in dominance-based neighborhood rough set[J].Information Scien-ces,2016,373:351-368.
[14]LIANG J Y,SHI Z,LI D,et al.Information entropy,rough entropy and knowledge granulation in incomplete information systems [J].International Journal of General Systems,2006,35(6):641-654.
[15]HU Q H,GUO M Z,YU D R,et al.Information entropy for ordinal classification[J].Science China Information Sciences,2010,53(6):1188-1200.
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