Computer Science ›› 2022, Vol. 49 ›› Issue (4): 161-167.doi: 10.11896/jsjkx.210500211

• Database & Big Data & Data Science • Previous Articles     Next Articles

Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost

WANG Zi-yin1,3, LI Lei-jun2,3,4, MI Ju-sheng2,3, LI Mei-zheng1, XIE Bin1   

  1. 1 College of Computer and Cyberspace Security, Hebei Normal University, Shijiazhuang 050024, China;
    2 College of Mathematical Sciences, Hebei Normal University, Shijiazhuang 050024, China;
    3 Hebei Key Laborotory of Computational Mathematics and Applications, Shijiazhuang 050024, China;
    4 Postdoctoral Research Workstation of Mathematics, Hebei Normal University, Shijiazhuang 050024, China
  • Received:2021-05-29 Revised:2021-10-21 Published:2022-04-01
  • About author:WANG Zi-yin,born in 1997,master.Her main research interests include granular computing and rough set.LI Lei-jun,born in 1985,Ph.D,associate professor,is a member of China Computer Federation and CAAI-CGCKD.His main research interests include granular computing and ensemble learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61502144,62076088),Natural Science Foundation of Hebei Province(F2018205196,F2019205295),Natural Science Foundation of Higher Education Institutions of Hebei Province(BJ2019014),Postdoctoral Advanced Programs of Hebei Province(B2016003013) and Training Funds for 333 Talents Project in Hebei Province(A2017002112).

Abstract: Attribute reduction is a hot research issue in rough set.In this paper, how to reduce redundant attributes without increasing the misclassification cost is studied.Firstly, the minimum misclassification degree of variable precision fuzzy rough sets is defined.Then, by introducing the decision process, the variable precision fuzzy rough set model is proposed based on the minimum misclassification degree.Then, a heuristic attribute reduction algorithm is proposed by taking the misclassification cost as an invariant.We compare this algorithm with other algorithms through experiments.The results show that the attribute reduction results obtained by the proposed algorithm have the advantages of less reserved attributes and lower misclassification cost.

Key words: Attribute reduction, Misclassification cost, Rough set, Variable precision fuzzy rough set

CLC Number: 

  • TP182
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