期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

乐观多粒度区间集粗糙集 被引量：3

Optimistic Multi-granulation Interval-set Rough Sets
下载PDF
导出
摘要 Pawlak粗糙集基于单个粒空间(一个等价关系)建立了上、下近似来刻画目标概念,而乐观多粒度粗糙集则利用多个粒空间(一族等价关系)对目标概念进行近似描述,是Pawlak粗糙集的一种扩展.区间集通过上、下界给出了概念的外延范围.在区间集粗糙集的基础上,提出了乐观多粒度区间集粗糙集,研究了它们的性质,并进一步给出了单个和多个粒空间下几种区间集粗糙集和乐观多粒度区间集粗糙集之间的关系. Pawlak′s rough sets showed the lower and upper approximations of a target concept by using a single granular space(an equivalence relation).The optimistic multi-granulation rough sets,an extension of Pawlak′s rough sets,provided the approximated description of a target concept based on multiple granular spaces(a family of equivalence relations).An interval-set was given by using the lower and upper bounds to investigate the extension of a concept.Based on the interval-set rough set,optimistic multi-granulation interval-set rough sets were introduced.Related properties of them were discussed.Furthermore,the relationships among several interval-set rough sets constructed in different single-granular spaces and optimistic multi-granulation interval-set rough sets in a multi-granular space were established.
作者 马建敏 景嫄 MA Jianmin;JING Yuan(Department of Mathematics and Information Sciences,Faculty of Science, Chang′an University,Xi′an 710064,China)
机构地区 长安大学理学院数学与信息科学系
出处 《郑州大学学报（理学版）》 CAS 北大核心 2018年第3期87-93,共7页 Journal of Zhengzhou University:Natural Science Edition
基金 国家自然科学基金项目(10901025 11501048 61772019)
关键词 区间集 区间集粗糙集 乐观多粒度粗糙集 乐观多粒度区间集粗糙集 interval set interval-set rough set optimistic multi-granulation rough set optimistic multi-granulation interval-set rough set
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献3

二级参考文献36

  • 1Khanafer R M, Solana B, Triola J, et al. Automated diagnosis for UMTS networks using Bayesian network approach [ J ]. IEEE Transactions on Vehicular Technology,2008,57 (4) : 2451 - 2461.
  • 2Frankel J, Wester M, King S. Articulatory feature recognition using dynamic Bayesian networks [ J ]. Computer Speech and Lan- guage,2007,21 (4) : 620 -640.
  • 3Charitos T, van der Gaag L C, Visscher S, et al. A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients [ J ]. Expert Systems with Applications,2009,36 (2) : 1249 - 1258.
  • 4Cai Zhiqiang, Sun Shudong, Si Shubin, et al. Identifying product failure rate based on a conditional Bayesian network classifier [ J]. Expert Systems with Applications ,2011,38 ( 5 ) : 5036 - 5043.
  • 5Hsieh N C, Hung L P. A data driven ensemble classifier for credit scoring analysis [ J]. Expert Systems with Applications ,2010, 37 ( 1 ) : 534 - 545.
  • 6Webb G I, Boughton J R, Wang Zhihai. Not so naive Bayes: aggregating one-dependence estimators [ J ]. Machine Learning, 2005,58(1) : 5 -24.
  • 7Zhang H, Jiang Liangxiao, Su Jiang. Hidden naive Bayes [ C ]//Proceedings of the 20th National Conference on Artificial Intelli- gence. London,2005 : 919 -924.
  • 8Zhang H, Jiang Liangxiao, Su Jiang. Augmenting naive Bayes for ranking[ C ]//Proceedings of the 22nd International Conference on Machine Learning. New York, 2005:1020- 1027.
  • 9Geiger D, Heckerman D. Knowledge representation and inference in similarity networks and Bayesian muhinets[ J]. Artificial In- telligence, 1996,82 ( 1/2 ) : 45 - 74.
  • 10Tschiatschek S, Reinprecht P, Mocke M, et al. Bayesian network classifiers with reduced precision parameters [ C ]//Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, Bristol,2012 : 74 - 89.

共引文献307

同被引文献4

引证文献3

二级引证文献5

郑州大学学报（理学版）
2018年 第3期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部