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稀有类分类问题研究 被引量：6

Research on Bare-class Classification
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摘要 探讨了稀有类问题的区分性、多态性、稀有性等主要特征,详述了稀有类分类算法的评估标准。研究了目前分类稀有类的四种方法,首次提出使用基本显露模式分类稀有类的思想。
作者 刘艳霞 职为梅 杨亮
机构地区 郑州大学信息工程学院 河南理工大学计算机科学系
出处 《微型机与应用》 北大核心 2005年第6期54-56,共3页 Microcomputer & Its Applications
关键词 数据挖掘 分类 稀有类 显露模式
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献6

  • 1Provost F.Machine learning from imbalanced data sets 101/1,in AAAI Workshop on Learning from Imbalanced Data Sets.Tech Rep.WS-00-05, Menlo Park, CA : AAAI Press, 2000.
  • 2Agarwal R,Joshi M V.PNrule: a New Framework for Learning Classifier Models in Data Mining(A Case-Study in Network Intrusion Detection).Technical Report RC 21719,IBM Research Report, Computer Science/Mathematics,2000 ; (4).
  • 3Alhammady H, Ramamohanarao K.The Application of Emerging Patterns for Improving the Quality of Rare-class Classification.ln :Proceedings of the 8th Pacific-Asia Conf.on Advances in Knowledge Discovery and Data Mining (PAKDD2004), Sydney, Australia, 2004.
  • 4Joshi M V, Agarwal R C, Kumar V.Predicting Rare Classes:Comparing Two-Phase Rule Induction to Cost-Sensitive Boosting.ln :Proceedings of the sixth European Conference on Priciples and Practice of Knowledge Discovery in Databases(PKDD2002), Helsinki, Finland, 2002.
  • 5范明,刘孟旭,赵红领.一种基于基本显露模式的分类算法[J].计算机科学,2004,31(11):211-214. 被引量：11
  • 6HanJ KamberM 数据挖掘 范明 孟小峰 译.概念与技术[M].北京:机械工业出版社,2001..

二级参考文献10

  • 1Blake C, Merz C. UCI repository of machine learning databases.1998 [http://www. ics. uci. edu/- mlearn/ MLRepository.html]. Irvine,CA: University of California,Department of Information and Computer Science
  • 2Dong G,Li J. Efficient mining of emerging patterns: Discovering trends and differences. In: Proc. of KDD′99, San Diego, USA,Sept. 1999.15-18
  • 3Dong G,Zhang X,Wong L,Li J. CAEP: Classification by Aggregating emerging patterns. In:Proc. of the 2nd Int′l Conf. On Discovery Science (DS′99) ,Tokyo,Japan,Dec. 1999.30-42
  • 4Fan H,Ramamohanarao K. Bayesian Approach to use Emerging Patterns for Classification. In:Proc of 14th Australasian Database Conf. Feb. 2003. 39-48
  • 5Han J,Pei J,Yin Y. Mining frequent patterns without candidate generation. In:Proc. of the 2000 ACM-SIGMOD Intl. Conf. on Management of Data, May 2000. 1-2
  • 6Li J,Dong G, Ramamohanarao K. JEP-Classifier: Classification by Aggregating Jumping Emerging Patters. Knowledge and Information Systems, 2001,3(2): 131-145
  • 7Li J,Dong G. Ramamohanarao K. Making Use of the Most Expressive Jumping Emerging Patterns for Classification. In:Pro. of 2000 Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD′00) ,2000. 220-223
  • 8Li W,Han J,Pei J. CMAR: Accurate and efficient classification based on multiple class-association rules. In:ICDM′01,San Jose,CA,Nov. 2001. 369-376
  • 9Lin B, Hsu W, Ma Y. Integrating classification and association rule mining. In:KDD′98,New York,NY,Aug. 1998. 80-86
  • 10Zheng Z, Webb G I. Lazy learning of Bayesian rules. Machine Learing, 41:53-84

共引文献13

同被引文献68

引证文献6

二级引证文献34

微型机与应用
2005年 第6期

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