摘要
利用模糊集理论扩展Ziarko的变精度粗糙集模型,得到变精度模糊粗糙数据模型(VPFRDM).以属性重要性为启发式信息,提出了模糊信息系统中的属性约简方法.通过计算各模糊模式类相对于决策类的分类能力,生成相应的模糊规则.仿真实验表明,与Ziarko的变精度粗糙集方法相比,VPFRDM具有更好的数据概括能力.
A variable precision fuzzy rough data model （VPFRDM） that is an extension of Ziarko＇s variable precision rough set （VPRS） model was proposed based on fuzzy set. A method for attribute reduction from a fuzzy decision information system was developed, in which the attribute importance is taken as the heuristic information. Fuzzy knowledge is extracted through calculating classification ability of each fuzzy pattern class to the decision categories. Simulation results show that the VPFRDM is effective and has better data generalization ability compared with Ziarko＇s VPRS.
出处
《西南交通大学学报》
EI
CSCD
北大核心
2008年第5期582-587,共6页
Journal of Southwest Jiaotong University
基金
国家自然科学基金资助项目(60573068
60773113)
重庆市自然科学基金重点项目(2008BA2017)
关键词
知识获取
可变精度粗糙集
模糊集
数据分析
模型
knowledge extraction
variable precision rough set
fuzzy set
data analysis
model
