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《山东大学学报(理学版)》 ›› 2022, Vol. 57 ›› Issue (5): 1-10.doi: 10.6040/j.issn.1671-9352.4.2021.250

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基于Bayesian直觉模糊粗糙集的数据分类方法

薛占熬1,2*,李永祥1,2,姚守倩1,2,荆萌萌1,2   

  1. 1.河南师范大学计算机与信息工程学院, 河南 新乡 453007;2.智慧商务与物联网技术河南省工程实验室, 河南 新乡 453007
  • 发布日期:2022-05-27
  • 作者简介:薛占熬(1963— ),男,教授,博士,研究方向为人工智能基础理论、粗糙集理论和三支决策理论. E-mail:xuezhanao@163.com*通信作者
  • 基金资助:
    国家自然科学基金资助项目(62076089,61772176);河南省科技攻关项目(182102210078,212102210136)

Data classification method based on Bayesian intuitionistic fuzzy rough sets

XUE Zhan-ao1,2*, LI Yong-xiang1,2, YAO Shou-qian1,2, JING Meng-meng1,2   

  1. 1. College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, Henan, China;
    2. Engineering Lab of Intelligence Business &
    Internet of Things, Henan Province, Xinxiang 453007, Henan, China
  • Published:2022-05-27

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摘要： 在直觉模糊集和粗糙集理论基础上,结合Bayesian概率和近似关系,提出了Bayesian直觉模糊粗糙集模型,并对其进行研究。首先,在粗糙集的基础上,定义了基于直觉模糊粗糙集上的Bayesian概率,结合直觉模糊近似关系和模糊矩阵,给出了直觉模糊等价关系,并讨论了其性质;其次,根据直觉模糊集和截集的特性,得到基于Bayesian直觉模糊粗糙集的等价类,并进一步给出了上、下近似的划分方法,求出正、负域和边界域并计算近似精度;最后,在UCI数据集上,分析验证该模型的有效性,该模型能较好地分类含有模糊信息的数据。

关键词: Bayesian粗糙集, 直觉模糊集, 直觉模糊等价关系, 近似精度, 数据分类

Abstract: This paper proposed a Bayesian intuitionistic fuzzy rough set model based on the theory of intuitionistic fuzzy sets and rough sets, Bayesian probability and approximate relations is combined, and conducted research on it. Firstly, on the basis of rough sets, Bayesian probability based on intuitionistic fuzzy rough set is defined, combined with intuitionistic fuzzy approximation relation and fuzzy matrix, the intuitionistic fuzzy equivalence relation is given, and its properties are discussed. Secondly, according to the characteristics of intuitionistic fuzzy sets and cut sets, the equivalence class basis of Bayesian intuitionistic fuzzy rough sets is obtained, and the upper and lower approximation division method are further given, the positive, negative and boundary fields are calculated and the approximate accuracy is calculated. Finally, the effectiveness of the model is analyzed and verified, and the data with fuzzy information can be better classified on the UCI data sets.

Key words: Bayesian rough set, intuitionistic fuzzy set, intuitionistic fuzzy equivalence relation, approximate accuracy, data classification

中图分类号: 

  • TP391
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