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F-粗糙集方法对概念漂移的度量 被引量：11

The F-rough sets approaches to the measures of concept drift
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摘要 F-粗糙集是一种针对信息表簇或决策表簇的新的粗糙集模型.利用F-粗糙集模型中上、下近似的思想,定义了不确定性概念漂移的一些度量,包括概念的上、下近似漂移量和概念的上、下近似耦合度等,并初步探讨了它们的性质. F-rough set model was regarded as a new rough set model for a family of information systems and decision systems. It was defined some measures of concept drift with the idea of upper approximation and lower approximation in F-rough sets. These measures included the measures for concept drift of upper approximation and lower approximation, the coincidence degrees for concept drift of upper approximation and lower approxi- mation etc. Moreover, some properties of these measures were investigated
作者 邓大勇 裴明华 黄厚宽
机构地区 浙江师范大学数理与信息工程学院 北京交通大学计算机与信息技术学院
出处 《浙江师范大学学报（自然科学版）》 CAS 2013年第3期303-308,共6页 Journal of Zhejiang Normal University:Natural Sciences
关键词 F-粗糙集 上近似 下近似 概念漂移 度量 F-rough sets upper approximation lower approximation concept drift measure
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献16

  • 1Pawlak Z. Rough sets-theoretical aspect of reasoning about data[ M ]. Dordrecht:Kluwer Academic Publishers, 1991.
  • 2Bazan G J. A comparison of dynamic and non-dynamic rough set methods for extracting laws from decision tables [ C ]//Polkowski L, Skowron A. Rough sets in knowledge discovery 1 :Methodology and applications. Heidelberg: Physica-Verlag, 1998:321-365.
  • 3Bazan G J,Nguyen H S,Nguyen S H,et al. Rough set algorithms in classification problem[ C]//Polkowski L,Tsumoto S,Lin T Y. Rough set methods and applications. Heidelberg: Physica-Verlag, 2000:49-88.
  • 4Inuiguchi M, Miyajima T. Variable precision rough set approach to multiple decision tables [ C ]//Slzak D, Wang Guoyin, Szczuka M. Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Heidelberg: Springer-Verlag,2005 : 304 -313.
  • 5Inuiguchi M. A Multi-agent rough set model toward group decision analysis[ J]. Kansei Engineering International Journal ,2006,6( 3 ) :33-40.
  • 6Inuiguchi M, Miyajima T. Rough set based rule induction from two decision tables [ J ]. European Journal of Operational Research, 2007,181 (3) :1540-1553.
  • 7Inuiguchi M. Three approaches to rule induction from multiple decision tables [ C ]//The Twelfth Czech Japan Seminar on Data Analysis and Decision Making under Uncertainty. Litomysl: Czech Republic, 2009:41-50.
  • 8]邓大勇,王基一.并行约简的现状与发展[J].中国人工智能学会通讯,201l,I(5):16.18.
  • 9Deng Dayong, Wang Jiyi, Li Xiangjun. Parallel reducts in a series of decision subsystems [ C ]//Proceedings of the Second International Joint Conference on Computational Sciences and Optimization. Sanya:City University of Hong Kong,2009:377-380.
  • 10Deng Dayong, Yan Dianxun, Wang Jiyi. Parallel reducts based on attribute significance [ C ]//Jian Yu, Salvatore G, Pawan L. Rough set and knowledge technology. Heidelberg: Springer-Verlag,2010:336-343.

二级参考文献42

  • 1Widmer G, Kubat M. Learning in the presence of concept drift and hidden contexts. Machine Learning, 1996, 23(1): 69-101.
  • 2Han J W, Kamber M. Data Mining: Concepts and Technique, 2nd edition. San Francisco.. Morgan Kaufman Publishers, 2006.
  • 3Gaber M M, Zaslavsky A, Krishnaswamy S. Mining data streams: a review. Sigmod Record, 2005, 34 (2): 18-26.
  • 4Yao Y Y. On modeling data mining with granu- lar computing. Proceedings of the 25^th Annual International Computer Software and Applica- tions Conference, 2001: 638-643.
  • 5Yao Y Y. Granular computing for data mining Proceedings of SPIE Conference on Data Min ing, Intrusion Detection, Information Assur ance,and Data Networks Security,2006: 1-12.
  • 6Zhu X D, Huang Z Q. Conceptual modeling rules extracting for data streams. Knowledge Based Systems, 2008, 21:934 - 940.
  • 7Wang Z J, Zhan D C. A component retrieval method based on feature matching. International Journal of Information Technology, 2006, 2 (8):60-72.
  • 8Widmer G,Kubat M. Learning in the Presence of Concept Drift and Hidden Contexts [J]. Machine Learning, 1996, 23 (1): 69- 101.
  • 9Schlirnmer J C, Granger R H. Incremental Leaning from Noisy Data[J]. Machine Learning, 1986,1 (3) : 317-354.
  • 10Wang H X,Fan W,Yu P S,et al. Mining Concept-Drifting Data Streams Using Eensemble Classifiers [C]// Proc of 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington.. ACM Press,2003.. 226-235.

共引文献37

同被引文献81

  • 1李德毅,孟海军,史雪梅.隶属云和隶属云发生器[J].计算机研究与发展,1995,32(6):15-20. 被引量：1212
  • 2邓大勇,黄厚宽,李向军.不一致决策系统中约简之间的比较[J].电子学报,2007,35(2):252-255. 被引量：28
  • 3程昳,苗夺谦,冯琴荣.基于模糊粗糙集的粒度计算[J].计算机科学,2007,34(7):142-145. 被引量：4
  • 4王国胤,李德毅,姚一豫,等.云模型与粒计算[M].北京:科学出版社,2012.
  • 5王涛,李舟军,颜跃进,陈火旺.数据流挖掘分类技术综述[J].计算机研究与发展,2007,44(11):1809-1815. 被引量：40
  • 6杨明.决策表中基于条件信息熵的近似约简[J].电子学报,2007,35(11):2156-2160. 被引量：44
  • 7Babcock B, Babu S, Dater M, et al. Models and issues in data stream systems [C] //Proc of the 21st ACM SIGACT- SIGMOD-S1GART Syrup on Principles Database Systems. New York: ACM, 2002:1-30.
  • 8Gehrke J, Ganti V, Ramakrishnan R, et al. Boat-optimistic decision tree construction [C]//Proc of the 18th ACM SIGMOD Int Conf on Management of Data. New York: ACM, 1999: 169-180.
  • 9Domingos P, Hulten G. Mining high-speed data stream [C] //Proc of the 6th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2000:71-80.
  • 10Hulten G, Spencer L, Domingos P. Mining time-changing data sreams [C] //Proe of the 7th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining. New York: ACM, 2001: 97-106.

引证文献11

二级引证文献34

浙江师范大学学报（自然科学版）
2013年 第3期

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