期刊文献+

一种基于决策的数据补全方法 被引量:2

A Method of Recruiting Default in Data Based on the Decision
下载PDF
导出
摘要 一般来说 ,在从数据库中获得有用的知识之前 ,通过采样、收集而取得的原始数据不一定适合于直接运用于知识获取 ,通常要对原始数据进行加工处理 ,对有遗漏的信息资料就要补全。文章在对粗糙集理论研究的基础上 ,运用容差关系对含有决策的数据表进行了遗漏补缺 。 Generally speaking, before acquiring useful information from database, the original data which are got by sampling and collecting may not be applied to acquiring knowledge directly. Usually, original data need to be processed and default filled. Based on the profound research of rough set theory, the decision table is default filled by using tolerance relation, which proves efficient.
作者 傅媛
出处 《昆明理工大学学报(理工版)》 2003年第6期157-160,共4页 Journal of Kunming University of Science and Technology(Natural Science Edition)
关键词 数据补全 数据库 粗糙集 信息表 决策表 rough set fill default tolerance relation tolerance relation in decision tolerance relation out decision decision table algorithm
  • 相关文献

参考文献2

二级参考文献2

共引文献4

同被引文献15

  • 1朱小飞,卓丽霞.一种基于量化容差关系的不完备数据分析方法[J].重庆工学院学报,2005,19(5):23-25. 被引量:9
  • 2Grzymala-Busse J W,Fu M.A Comparison of several approaches to missing attribute values in data mining.In:Proceedings of the 2nd International Conference on Rough Sets and Current Trends in Computing,Berlin:Springer-Verlag,2000.378~385
  • 3Wong K C,Chiu K Y.Synthesizing statistical knowledge for incomplete mixed-mode data.IEEE Trans.on Pattern Analysis and Machine Intelligence,1987,9:796~805
  • 4Kryszkiewicz M.Rough set approach to incomplete information system.Information Sciences,1998,112:39~49
  • 5Stefanowski J,Tsoukias A.On the extension of rough sets under incomplete information.In:Proceedings of The Seventh International Workshop on Rough Sets,Fuzzy Sets,Data Mining,and Granular-Soft Computing (RSFDGrC'99),1999.73~81
  • 6余英泽 王国胤 吴渝.一种基于Rough理论的不完备信息系统处理方法[J].计算机科学,2001,28(5):35-38.
  • 7Witten I H,MacDonald B A.Using concept learning for knowledge acquisition.International Journal of Man-Machine Studies,1988,27:349~370
  • 8Qi Yu, Yoan Miche. Regularized extreme learning machine for regression with missing data. Neurocomputing, 2013, 102:45-51.
  • 9Lakshminarayan K, et al. Imputation of missing data in industrial databases. Applied Intelligence, 1999, 11:259-275.
  • 10Gerhard Tutz, Shahla Ramzan. Improved methods for the imputation of missing data by nearest neighbor methods. Computational Statistics and Data Analysis, 2015, 90:84-99.

引证文献2

二级引证文献19

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