基于模糊邻域粗糙集的信息系统不确定性度量方法

南京大学学报(自然科学版) ›› 2017, Vol. 53 ›› Issue (5): 926–.

基于模糊邻域粗糙集的信息系统不确定性度量方法

徐　风1，2，姚　晟1，2*，纪　霞1，2，赵　鹏1，2，汪　杰1，2

出版日期:2017-09-25 发布日期:2017-09-25
作者简介:1.安徽大学计算机科学与技术学院，合肥，230601；
2．安徽大学计算智能与信号处理教育部重点实验室，合肥，230601
基金资助:
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
收稿日期：2017－06－06
*通讯联系人，E－mail：fisheryao@126.com

Uncertainty measurement method for information system based on fuzzy neighborhood rough set

Xu Feng1，2，Yao Sheng1，2*，Ji Xia1，2，Zhao Peng1，2，Wang Jie1，2

Online:2017-09-25 Published:2017-09-25
About author:1.College of Computer Science and Technology，Anhui University，Hefei，230601，China；
2.Key Laboratory of Intelligent Computing & Signal Processing，Ministry of Education，Anhui University，Hefei，230601，China

摘要/Abstract

摘要： 邻域粗糙集和模糊粗糙集是粗糙集理论中处理数值型数据的两种重要模型．在数值型信息系统中融合两者在不确定性度量方面的优越性，首先引入了模糊邻域粗糙集模型，并在该模型上定义了模糊邻域粗糙度的概念．模糊邻域粗糙度是通过粗糙集的边界域来度量信息系统的不确定性，为了达到更为全面的度量效果，在模糊邻域粗糙集模型中定义了模糊邻域粒结构，并基于该粒结构提出了模糊邻域粒度的概念，模糊邻域粒度是对信息系统分类能力的一种度量．最后，通过将两种度量方法进行结合，提出了一种基于模糊邻域粗糙集的混合不确定性度量方法，并从理论上证明其有效性．实验结果表明，所提出的混合度量方法综合了两种单独度量方法的优点，在数值型信息系统中具有更好的度量效果，因此所提出的不确定性度量方法更具有一定的优越性．

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.

徐　风1，2，姚　晟1，2*，纪　霞1，2，赵　鹏1，2，汪　杰1，2. 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–.

Xu Feng1，2，Yao Sheng1，2*，Ji Xia1，2，Zhao Peng1，2，Wang Jie1，2. Uncertainty measurement method for information system based on fuzzy neighborhood rough set[J]. Journal of Nanjing University(Natural Sciences), 2017, 53(5): 926–.

参考文献

[1]　Pawlak Z．Rough sets.International Journal of Computer & Information Sciences，1982，11(5)：341－356.
[2] Liang J Y，Wang F，Dang C Y，et al.A group incremental approach to feature selection applying rough set technique.IEEE Transactions on Knowledge and Data Engineering，2014，26(2)：294－308.
[3] Fong S，Wong R，Vasilakos A V．Accelerated PSO swarm search feature selection for data stream mining big data.IEEE Transactions on Services Computing，2016，9(1)：33－45.
[4] 何松华，康婵娟，鲁　敏等．基于邻域组合测度的属性约简方法．控制与决策，2016，31(7)：1225－1230.(He S H，Kang C J，Lu M，et al.Attribute reduction method based on neighborhood combination measure.Control and Decision，2016，31(7)：1225－1230.)
[5] 滕书华，鲁　敏，杨阿锋等．基于一般二元关系的粗糙集加权不确定性度量．计算机学报，2014，37(3)：649－665.(Teng S H，Lu M，Yang A F，et al.A weighted uncertainty measure of rough sets based on general binary relation.Chinese Journal of Computers，2014，37(3)：649－665.)
[6] Zheng T T，Zhu L Y．Uncertainty measures of neighborhood system－based rough sets.Knowledge－Based Systems，2015，86：57－65.
[7] Huang B，Zhuang Y L，Li H X．Information granulation and uncertainty measures in interval－valued intuitionistic fuzzy information systems.European Journal of Operational Research，2013，231(1)：162－170.
[8] Qian Y H，Liang J Y，Wu W Z，et al.Information granularity in fuzzy binary GrC model.IEEE Transactions on Fuzzy Systems，2011，19(2)：253－264.
[9] Liang J Y，Qian Y H．Information granules and entropy theory in information systems.Science in China Series F：Information Sciences，2008，51(10)：1427－1444.
[10] Lin T Y．Rough sets，neighborhood systems and approximation.World Journal of Surgery，1986，10(2)：189－194.
[11] Dubois D，Prade H．Rough fuzzy sets and fuzzy rough sets.International Journal of General Systems，1990，17(2－3)：191－209.
[12] Wang C Z，Shao M W，He Q，et al.Feature subset selection based on fuzzy neighborhood rough sets.Knowledge－Based Systems，2016，111：173－179.
[13] 黄国顺，文　翰．基于边界域和知识粒度的粗糙集不确定性度量．控制与决策，2016，31(6)：983－989.(Huang G S，Wen H．Uncertainty measures of rough sets based on boundary region and knowledge granularity.Control and Decision，2016，31(6)：983－989.)
[14] Chen D G，Zhang X X，Li W L．On measurements of covering rough sets based on granules and evidence theory.Information Sciences，2015，317：329－348.
[15] Pedrycz A，Hirota K，Pedrycz W，et al.Granular representation and granular computing with fuzzy sets.Fuzzy Sets and Systems，2012，203：17－32.
[16] Zhang X Y，Miao D Q．Quantitative information architecture，granular computing and rough set models in the double－quantitative approximation space of precision and grade.Information Sciences，2014，268：147－168.

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