|Table of Contents|

Stable attribute reduction approach for fuzzy rough set(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年01期
Page:
68-
Research Field:
Publishing date:

Info

Title:
Stable attribute reduction approach for fuzzy rough set
Author(s):
Li Jingzheng1Yang Xibei12Wang Pingxin3Chen Xiangjian1
1.School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212003,China; 2.School of Economics & Management,Nanjing University of Science and Technology,Nanjing 210094,China; 3.School of Mathematics and Physics,Jiangsu Unive
Keywords:
attribute reduction data perturbation fuzzy rough sets stability
PACS:
TP18
DOI:
10.14177/j.cnki.32-1397n.2018.42.01.010
Abstract:
Attribute reduction plays a core role in rough set theory.Presently,most of the results of such topic are based on the measurements such that classification performances,costs,uncertainties and so on.Those do not carefully take the fluctuations of reducts into account if data perturbations happen.To fill this gap,a heuristic framework for generating stable reduct is proposed.Firstly,multiple boundary sample sets are induced by multiple clusterings’ technique.Secondly,the fused significance for each attribute can be computed using the multiple significances of such attribute obtained in all boundary sample sets.Finally,the attribute with greatest fused significance is selected and then added into the pool set.The proposed algorithm is tested on several UCI data sets and the experimental results indicate that by comparing with traditional heuristic algorithms,this approach can not only effectively improve the time efficiency for computing reduct and the stability of the reduct,but also advance the classification stability based on the reduct.

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Last Update: 2018-02-28