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。
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.
作者
王子茵
李磊军
米据生
李美争
解滨
WANG Zi-yin;LI Lei-jun;MI Ju-sheng;LI Mei-zheng;XIE Bin(College of Computer and Cyberspace Security,Hebei Normal University,Shijiazhuang 050024,China;College of Mathematical Sciences,Hebei Normal University,Shijiazhuang 050024,China;Hebei Key Laborotory of Computational Mathematics and Applications,Shijiazhuang 050024,China;Postdoctoral Research Workstation of Mathematics,Hebei Normal University,Shijiazhuang 050024,China)
出处
《计算机科学》
CSCD
北大核心
2022年第4期161-167,共7页
Computer Science
基金
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)。
关键词
粗糙集
变精度模糊粗糙集
误分类代价
属性约简
Rough set
Variable precision fuzzy rough set
Misclassification cost
Attribute reduction
