计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 161-167.doi: 10.11896/jsjkx.210500211

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于误分代价的变精度模糊粗糙集属性约简

王子茵1,3, 李磊军2,3,4, 米据生2,3, 李美争1, 解滨1   

  1. 1 河北师范大学计算机与网络空间安全学院 石家庄 050024;
    2 河北师范大学数学科学学院 石家庄 050024;
    3 河北省计算数学与应用重点实验室 石家庄 050024;
    4 河北师范大学数学博士后科研流动站 石家庄 050024
  • 收稿日期:2021-05-29 修回日期:2021-10-21 发布日期:2022-04-01
  • 通讯作者: 李磊军(lileijun1985@163.com)
  • 作者简介:(1060034724@qq.com)
  • 基金资助:
    国家自然科学基金(61502144,62076088); 河北省自然科学基金(F2018205196,F2019205295); 河北省高等学校自然科学基金(BJ2019014); 河北省博士后择优资助科研项目(B2016003013); 河北省三三三人才工程培养经费(A2017002112)

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).

摘要: 属性约简目前是粗糙集领域的热点研究问题。文中研究了如何在保持误分类代价不增加的基础上减少冗余属性。首先定义了变精度模糊粗糙集中的最小误分类程度,然后引入了决策过程,提出了一种基于最小误分类程度的变精度模糊粗糙集模型,最后在这个模型的基础上将误分代价作为不变量,提出了一种启发式属性约简算法。将所提算法与其他算法进行比较,实验结果表明,所提算法得到的属性约简结果具有保留的属性数相对较少、误分类代价更低的优点。

关键词: 变精度模糊粗糙集, 粗糙集, 误分类代价, 属性约简

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

中图分类号: 

  • TP182
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