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

信息表离散格的进一步研究 被引量：2

Further Analysis of Discretization Lattice
原文传递
导出
摘要 在文献[1]的基础上,得到了离散格的表示定理。进一步证明了在离散格到划分格的映射下交运算可以保持运算,而并运算不能保持运算,因此该映射不是同态映射。根据离散化后得到的对象域划分定义了离散化方案之间的等价关系,证明了随着离散化等价类[DR]的加粗,离散化方案对应的正区域下降,而条件信息熵上升,最后分析了另外两种离散格搜索算法。 Based on Reference [1], this paper obtains the representation theorem of discretization lattice and proves that the mapping from discretization lattice to partition lattice is not a homomor-phism. An equivalence relation between discretization schemes is defined, and further analysis shows that the positive region of conditional attributes decreases and the conditional entropy increases if the discretization schemes coarsen. Finally, another two searching algorithms for discretization lattice are analyzed.
作者 王立宏 吴耿锋
机构地区 烟台大学计算机学院 上海大学计算机工程与科学学院
出处 《模式识别与人工智能》 EI CSCD 北大核心 2005年第1期25-30,共6页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金(No.60203011)
关键词 同态映射 原子 离散格搜索 Homomorphism Atom Searching for Discretization Lattice
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9

  • 1李刚,童頫.基于混合概率模型的无监督离散化算法[J].计算机学报,2002,25(2):158-164. 被引量：16
  • 2王立宏,吴耿锋.基于并行协同进化的属性约简[J].计算机学报,2003,26(5):630-635. 被引量：22
  • 3王立宏,吴耿锋.信息表的离散格研究[J].模式识别与人工智能,2004,17(1):11-16. 被引量：3
  • 4Bay S D. Detecting Group Differences: Mining Contrast Sets.Data Mining and Knowledge Discovery, 2001, 5 : 213-246.
  • 5Bay S D. Multivariant Discretization for Set Mining. Knowledge and Information Systems, 2001, 3: 491-512.
  • 6Nguyen H S, Skowron A. Quantization of Real Value Attributes. In: Wang P P, ed. Proc of the 2nd Annual Joint Conference on Information Sciences. Wrightsville Beach, USA, 1995,34-37.
  • 7Bazan J G. A Comparison of Dynamic and Non-Dynamic Rough Set Methods for Extracting Laws from Decision Tables. In:Polkowski L, Skowron A, eds. Rough Sets in Knowledge Discovery 1: Methodology and Applications, Berlin, Germany:Springer-Verlag, 1998, 322- 365.
  • 8Wang G Y. Rough Reduction in Algebra View and Information View. International Journal of Intelligent Systems, 2003, 18: 679-688.
  • 9Wang L H, Wu G F. Attribute Reduction and Information Granularity. In: Proc of the 6th World Multi-Conference on Systemics, Cybernetics and Informatics. Orlando, USA, 2002,168- 172.

二级参考文献26

  • 1[1]Catlett J. On changing continuous attributes into ordered discreteattributes. In: Proc European Working Session on Learning (EWSL91). LNAI-482, Porto,Portugal, 1991. 164-178
  • 2[2]Dougherty J, Kohavi R, Sahami M. Supervised and unsupervised discretizationof continuous features. In: Proc the 12th International Conference, Morgan KaufmannPublishers, 1995.194-202
  • 3[3]Quinlan J R. C4.5: Programs for Machine Learning. San Mateo: Morgan Kaufmann,1993
  • 4[4]Fayyad U, Irani K. Multi-interval discretizaton of continuous-valuedattributes for classification learning. In: Proc the 13th International JointConference on Artificial Intelligence, San Mateo, CA. Morgan Kaufmann Publishers,1993. 1022-1027
  • 5[5]Li G, Tong F. WILD: Weighted information-loss discretization algorithm forordinal attributes. In: Proc Conference on Intelligent Information Processing, the16th IFIP World Computer Congress 2000, Beijing, China, 2000.254-527
  • 6[6]Quinlan J R. Improved use of continuous attributes in C4.5. Journal ofArtificial Intelligence Research, 1996,4(1):77-90
  • 7[7]Wong A K C, Chiu D K Y. Synthesizing statistical knowledge from incompletemixed-mode data. IEEE Trans Pattern Analysis and Machine Intelligence, 1987,PAMI-9(6):796-805
  • 8[8]Banfield J D, Raftery A E. Model based Gaussian and non-Gaussian clustering.Biometrics, 1993,49(3):803-821
  • 9[9]Mackay D J C. Information Theory, Inference and Learning Algorithms.Cambridge: Cambridge University Press, 2000
  • 10[10]Dempster A P, Laird N M, Rubin D B. Maximum likelihood for incomplete data viathe EM algorithm. Journal of the Royal Statistical Society, Series B, 1977,39(1):1-38

共引文献37

同被引文献13

  • 1耿志强,朱群雄,李芳.知识粗糙性的粒度原理及其约简[J].系统工程与电子技术,2004,26(8):1112-1116. 被引量：26
  • 2谢宏,程浩忠,牛东晓.基于信息熵的粗糙集连续属性离散化算法[J].计算机学报,2005,28(9):1570-1574. 被引量：134
  • 3Liu Li-li,Wong A K C.A global optimal algorithm for Class-dependent discretization of continuous data[J].Intelligent Data Analysis, 2004,8: 151-170.
  • 4Yao Y Y,Yao J T.Granular computing as a basis for consistent classification problems[C]//PAKDD Workshop on Foundation of Data Mining, Taipei, Taiwan, 2002 ,5 ( 2 ) : 101-106.
  • 5Greco S,Matarazzo B,Slowinski R.Rough approximation by dominance relations[J].International Journal of Intelligent Systems,2002,17: 153-171.
  • 6Bay S D, Pazzani M. Detecting Group Differences: Mining Contrast Sets. Data Mining and Knowledge Discovery, 2001, 5 (3) : 213 - 246
  • 7Bay S D. Muhivariant Discretization for Set Mining. Knowledge and Information Systems, 2001, 3(4) : 491 -512
  • 8Nguyen H S, Skowron A. Quantization of Real Value Attributes: Rough Set and Boolean Reasoning Approach//Proc of the 2nd Annual Joint Conference on Information Sciences. Wrightsville Beach, USA, 1995:34-37
  • 9Liu Lili, Wong A K C, Wang Yang. A Global Optimal Algorithm for Class-Dependent Discretization of Continuous Data. Intelligent Data Analysis, 2004, 8(2): 151 -170
  • 10苗夺谦,王珏.粗糙集理论中概念与运算的信息表示[J].软件学报,1999,10(2):113-116. 被引量：250

引证文献2

二级引证文献1

模式识别与人工智能
2005年 第1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