期刊文献+

取值稠密信息系统的一种属性约简方法

An attribute reduction of dense domain information systems
下载PDF
导出
摘要 目的讨论取值稠密信息系统的一种属性约简方法。方法通过对取值稠密信息系统进行预处理——离散化,把它们转化为经典的信息系统,然后以有向关联信息作为度量标准对转化后所得的经典信息系统进行属性约简。结果/结论该方法可对取值稠密信息系统有效地进行属性约简,使此类信息系统的规则表示简单直观。 Aim An attribute reduction method of dense domain information systems was discussed. Methods Dense domain information systems was inverted to classic information systems by preconditioning——discretization.And we gave a method of attribute reduction of classic information systems using oriented relevance information. Results/Conclusion The test shows that this approach not only succeeds in giving dense domain information systems attribute reduction,but also gets effective conclusions.
作者 杨丽霞
出处 《宝鸡文理学院学报(自然科学版)》 CAS 2009年第3期68-72,共5页 Journal of Baoji University of Arts and Sciences(Natural Science Edition)
基金 宝鸡文理学院重点项目(ZK08137)
关键词 取值稠密信息系统 有向关联信息 属性约简 dense domain information systems oriented relevance information attribute reduction
  • 相关文献

参考文献7

  • 1FAYYAD U M, IRANI K B. Multi-interval dis cretization of continuous-valued attributes for clas sification learning[C]//Proc. Thirteenth Interna tional Joint Conference on Artificial Intelligence. Machine Learning, 1993: 1022-1027.
  • 2LIU H, SETIONO R. Chi2: Feature selection and discretization of numeric attributes [C]//Proceedings of the Seventh IEEE International Conference on Toolswith Artificial Intelligence. IEEE, 1995: 388-391.
  • 3DOUGHERTY J, KOHAVI R, SAHAMI M. Supervised and unsupervised discretization of continuous features[C]//Proceedings of theTwelfth International Conference. Machine Learning, 1995: 194- 202.
  • 4QUINLAN J R. Improved use of continuous attributes in C4.5[J]. Journal of Artificial Intelligence Research, 1996,4 : 77-90.
  • 5HO K M, SCOTT P D. Zeta: A global method for diseretization of continuous variables[C]//Proceedings of Third International Conference on Knowledge Discovery and Data Mining. AAAI press, 1997: 191-202.
  • 6LIU H, HUSSAIN F, TAN C L, et al. Discretization: An enabling technique[J]. Data Mining and Knowledge Discovery, 2002,6 : 393-423.
  • 7杨丽霞,魏立力.基于有向关联信息的知识约简[J].计算机工程与应用,2007,43(1):189-191. 被引量:3

二级参考文献6

  • 1李鸿.一种基于粗糙熵的知识约简算法[J].计算机工程与应用,2005,41(14):78-80. 被引量:11
  • 2魏玲,张文修.决策表分析的统计依据[J].计算机科学,2005,32(4):19-21. 被引量:3
  • 3Pawlak Z.Rough sets-theoretical aspects of reasoning about data[M].Boston.Kluwer Academic Publishers, 1991:9-70.
  • 4Zytkow J M.Granularity refined by knowledge:contingency tables and rough sets as tools of discovery proceedings of SPIE[C]//Data Mining and Knowledge Discovery:Theory,Tools and Technology,2000,2:82-91.
  • 5Wei Li-li,Zhang Wen-xiu.A heuristic algorithm for reduction of knowledge in probabilistic decision table based on fuzzy entropy[C]//Proceedings of the Second International Conference on Machine Learning and Cybernetics,Xi 'an,2003,11 : 1619-1623.
  • 6Slezak D.Approximate entropy eeducts[J].Fundamenta Informaticae,2002(12) : 365-390.

共引文献2

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