多粒度邻域粗糙直觉模糊集模型<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201701002

摘要
图/表
参考文献
相关文章 (6)

全文: PDF (621 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要针对名义型属性和数值型属性并存的混合型数据,结合多粒度邻域粗糙集和直觉模糊集,分别定义模糊覆盖粗糙隶属度和非隶属度.基于不同的属性集序列和不同的邻域半径,构建多粒度邻域粗糙直觉模糊集模型,证明模型相关性质.然后提出乐观和悲观多粒度邻域粗糙直觉模糊集的近似集,并讨论模型性质.最后使用文中模型计算实例,说明其能较好地解决名义型属性和数值型属性的混合型数据的处理问题.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	薛占熬
	司小朦
	袁艺林
	辛现伟

关键词 ：直觉模糊集, 多粒度粗糙集, 邻域粗糙集

Abstract：The combination of the multi-granulation neighborhood rough set and the intuitionistic fuzzy set is further researched in this paper. Firstly, the concepts of the intuitionistic fuzzy covering-based rough membership and non-membership are defined for dealing with the heterogeneous data including categorical attributes and numerical attributes. Secondly, a multi-granulation neighborhood rough intuitionistic fuzzy set model is established based on different attribute set sequences and different neighborhood radii. Then, the properties of multi-granulation neighborhood rough intuitionistic fuzzy set are discussed. Next, the approximate sets of the optimistic and pessimistic multi-granulation neighborhood rough intuitionistic fuzzy sets are constructed and their properties are discussed. Finally, these models are illustrated with examples. Example analysis shows the models can handle the heterogeneous data including categorical attributes and numerical attributes more accurately.

Key words： Intuitionistic Fuzzy Set Multi granularity Rough Set Neighborhood Rough Set

收稿日期: 2016-08-04

ZTFLH:

TP 181

基金资助:国家自然科学基金项目(No.61273018)、河南省基础与前沿技术研究计划项目(No.132300410174)、河南省教育厅计划项目(No.14A520082)、新乡市重点科技攻关计划项目(No.ZG14020)资助

作者简介: 薛占熬(通讯作者),男,1963年生,博士,教授,主要研究方向为人工智能基础理论、粗糙集理论.E-mail:xuezhanao@163.com.司小朦,女,1989年生,硕士研究生,主要研究方向为直觉模糊集理论、粗糙集理论.E-mail:27045020@qq.com.袁艺林,女,1991年生,硕士研究生,主要研究方向为直觉模糊集理论、粗糙集理论.E-mail:1292970082@qq.com.辛现伟,男,1991年生,硕士研究生,主要研究方向为直觉模糊集理论、粗糙集理论、决策论.E-mail:357202340@qq.com.

引用本文:

薛占熬,司小朦,袁艺林,辛现伟. 多粒度邻域粗糙直觉模糊集模型^*[J]. 模式识别与人工智能, 2017, 30(1): 11-20. XUE Zhan′ao, SI Xiaomeng, YUAN Yilin, XIN Xianwei. Model of Multi-granulation Neighborhood Rough Intuitionistic Fuzzy Sets. , 2017, 30(1): 11-20.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201701002 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2017/V30/I1/11

[1] PAWLAK Z. Rough Sets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[2] LIN T Y, HUANG K J, LIU Q, et al. Rough Sets, Neighborhood Systems and Approximation // Proc of the 5th International Sympo-sium on Methodologies of Intelligent Systems. London, UK: Springer, 1990: 130-141.
[3] 杨习贝,杨静宇.邻域系统粗糙集模型.南京理工大学学报(自然科学版), 2012, 36(2): 291-295.
(YANG X B, YANG J Y. Rough Set Model Based on Neighborhood System. Journal of Nanjing University of Science and Technology(Natural Science), 2012, 36(2): 291-295.)
[4] QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: A Multi-granulation Rough Set. Information Sciences, 2010, 180(6): 949-970.
[5] QIAN Y H, LIANG J Y, DANG C Y. Incomplete Multi-granulations Rough Set. IEEE Transactions on Systems, Man and Cybernetics(Systems and Humans), 2010, 40(2): 420-431.
[6] QIAN Y H, LIANG J Y, WEI W. Pessimistic Rough Decision. Journal of Zhejiang Ocean University(Natural Science), 2010, 29(5): 440-449.
[7] 张明,程科,杨习贝,等.基于加权粒度的多粒度粗糙集.控制与决策, 2015, 30(2): 222-228.
(ZHANG M, CHENG K, YANG X B, et al. Multigranulation Rough Set Based on Weighted Granulations. Control and Decision, 2015, 30(2): 222-228.)
[8] 马睿,刘文奇.基于集值信息系统的多粒度粗糙集.系统工程与电子技术, 2014, 36(5): 920-925.
(MA R, LIU W Q. Multi-granulation Rough Set Model Based on Set-Valued Information System. Systems Engineering and Electro-nics, 2014, 36(5): 920-925.)
[9] XU W H, WANG Q R, ZHANG X.T. Multi-granulation Rough Sets Based on Tolerance Relations. Soft Computing, 2013, 17(7): 1241-1252.
[10] 徐怡,杨宏健,纪霞.基于双重粒化准则的邻域多粒度粗糙集模型.控制与决策, 2015, 30(8): 1469-1478.
(XU Y, YANG H J, JI X. Neighborhood Multi-granulation Rough Set Model Based on Double Granulate Criterion. Control and Decision, 2015, 30(8): 1469-1478.)
[11] ATANASSOV K T. Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 1986, 20(1): 87-96.
[12] ATANASSOV K T. New Operations Defined over the Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 1994, 61(2): 137-142.
[13] ATANASSOV K T. More on Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 1989, 33(1): 37-45.
[14] WU X D, ZHU X Q, WU G Q, et al. Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(1): 97-107.
[15] PETERS G, CRESPO F, LINGRAS P, et al. Soft Clustering-Fuzzy and Rough Approaches and Their Extensions and Derivatives. International Journal of Approximate Reasoning, 2013, 54(2): 307-322.
[16] DAI J H, TIAN H W. Fuzzy Rough Set Model for Set-Valued Data. Fuzzy Sets and Systems, 2013, 229: 54-68.
[17] BONIKOWSKI Z, BRYNIARSKI E, WYBRANIEC-SKARDOWSKA U. Extensions and Intentions in the Rough Set Theory. Information Sciences, 1998, 107(1/2/3/4): 149-167.
[18] PAWLAK Z. Rough Sets: Theoretical Aspects of Reasoning about Data. Boston, USA: Kluwer Academic Publishers, 1991.

[19] 薛占熬,司小朦,朱泰隆,等.覆盖粗糙直觉模糊集模型的研究.计算机科学, 2016, 43(1): 44-48, 68.
(XUE Z A, SI X M, ZHU T L, et al. On the Model of Covering-Based Rough Intuitionistic Fuzzy Sets. Computer Science, 2016, 43(1): 44-48, 68.)
[20] 郭郁婷,李进金,李克典,等.多粒度覆盖粗糙直觉模糊集模型.南京大学学报(自然科学版), 2015, 51(2): 438-446.
(GUO Y T, LI J J, LI K D, et al. Multi-granulation Covering Rough-Intuitionistic Fuzzy Set Model. Journal of Nanjing University (Natural Science), 2015, 51(2): 438-446.)