基于正域向量的决策粗糙集属性约简
Attribute reduction in decision-theoretic rough set models based on positive region vector
黄国顺1
作者信息
- 1. 佛山科学技术学院数学与大数据学院,广东佛山 528000
- 折叠
摘要
在决策粗糙集模型中,现有划分层的正域都是通过集合求并所得,但基于该方法的保正域不变的属性约简与基于差别矩阵方法所得约简结果并不一致.提出了一种基于正域向量的决策粗糙集属性约简方法,该方法与基于差别矩阵的约简方法所得结果是一致的.最后给出一个算例说明其一致性.
Abstract
In the decision-theoretic rough set model,the existing positive region at the level of classification is integrated by union.However,the attribute reduction based on this method is not consistent with that based on discernibility matrix.This paper analyzes the reason in depth and proposes an attribute reduction based on positive region vector.It is proved that the positive region vector preservation is consistent with the results obtained by discernibility matrix method.Finally,an example is given to illustrate their consistency.
关键词
决策粗糙集模型/正域向量/属性约简/差别矩阵Key words
decision-theoretic rough set models/positive region vector/attribute reduction/discernibility matrix引用本文复制引用
基金项目
广东省基础与应用基础研究基金项目(2021B1515120048)
出版年
2024
国家科技期刊平台
NSTL
万方数据