南京大学学报（自然科学版）2017，Vol.53Issue(5)：926-936,11.DOI:10.13232/j.cnki.jnju.2017.05.012
基于模糊邻域粗糙集的信息系统不确定性度量方法
Uncertainty measurement method for information system based on fuzzy neighborhood rough set
摘要
Abstract
The neighborhood rough set and the fuzzy rough set are the two kind of important models for processing numeric data in rough set theory.In the numerical information system,combining with the superiority of the neighborhood rough set and the fuzzy rough set in terms of uncertainty measurement.The model of fuzzy neighborhood rough set is firstly introduced in this paper,and the conception of fuzzy neighborhood roughness is defined on the model of fuzzy neighborhood rough set.The fuzzy neighborhood roughness measures the uncertainty of information system through the boundary region of rough set,which is aimed to obtain more comprehensive measurement effect.And then,the fuzzy neighborhood granular structure is defined on the model of the fuzzy neighborhood rough set,and the concept of fuzzy neighborhood granularity is proposed based on the fuzzy neighborhood granular structure,and the fuzzy neighborhood granularity is a measure of the classification capacity for information system.At last,the method of hybrid uncertainty measurement based on fuzzy neighborhood rough set is proposed through combining two measurement methods,and which is theoretically proved effective,Experimental results show that the proposed method of hybrid measurement integrates the advantages of the two separate measurement methods,which has better effect of measurement.Therefore,the proposed method of uncertainty measurement has more certain superiority in this paper.关键词
不确定性度量/模糊邻域/近似粗糙度/模糊邻域粒度/混合度量Key words
uncertainty measurement/fuzzy neighborhood/approximation roughness/fuzzy neighborhood granulation/mixed measurement分类
信息技术与安全科学引用本文复制引用
徐风,姚晟,纪霞,赵鹏,汪杰..基于模糊邻域粗糙集的信息系统不确定性度量方法[J].南京大学学报（自然科学版）,2017,53(5):926-936,11.基金项目
国家自然科学基金(61602004,61300057),安徽省自然科学基金(1508085MF127),安徽省高等学校自然科学研究重点项目(KJ2016A041),安徽大学信息保障技术协同创新中心公开招标课题(ADXXBZ2014-6),安徽大学博士科研启动基金(J10113190072),安徽大学计算智能与信号处理教育部重点实验室课题 （61602004,61300057）
