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稀有类分类问题探讨 被引量：2

Research on Classification of Rare Classes
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摘要 分类是数据挖掘中的重要任务之一,稀有类分类问题是分类中的一个重要分支,可以描述为从一个分布极不平衡的数据集中标识出那些具有显著意义却很少发生的实例,在现实生活中的很多领域都有广泛的应用。详细地介绍了稀有类分类的问题,探讨了稀有类分类的一些特征、影响稀有类分类的一些因素和对稀有类分类进行评估的标准,介绍了当前分类稀有类的主要方法:基于数据集的方法和基于算法的方法。介绍了当前几种流行的稀有类分类算法。 Classification is an important task in data mining.Rare classification is a part of classification and it can be described as identifying the instance with statistical significance from imbalanced datasets.The classification of rarely occurring cases is widely used in many real life applications.Introduce the question of rare classification and discuss the features and general criteria of rare classification,and also study the popular methods to classify rare cases： based on data level and algorithm level.In the last introduce the popular algorithm of rare classification.
作者 职为梅 范明
机构地区 郑州大学信息工程学院
出处 《计算机技术与发展》 2010年第7期250-252,F0003,共4页 Computer Technology and Development
基金 河南省自然科学基金(0211050100)
关键词 分类 稀有类 显露模式 两阶段分类 classification rare class emerging pattern two-phase classification
分类号 TP301 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献12

  • 1HANJ KAMBERM.数据挖掘:概念与技术[M].北京:机械工业出版社,2001..
  • 2刘艳霞,职为梅,杨亮.稀有类分类问题研究[J].微型机与应用,2005,24(6):54-56. 被引量：6
  • 3Weiss G,Provost F. Learning when training data are costly:the effect of class distribution on tree induction[J]. J. Aritif. Intell. Res. ,2003,19:315 - 354.
  • 4Yan Min, Kamel M S, Wong A K C, et al. Cost - sensitive boosting for classification of imbalanced data [ J ]. Pattern Recognition, 2007 (10) : 3358 - 3378.
  • 5Agarwal R,Joshi M V. PNrttle:A new Framework for Learning Classifier Models in Data Mining (A Case - Study in Network Intrusion Detection) [ C ]//Proc. of the First SIAM Conference on Data Mining. Chicago,USA: [s. n. ] ,2001.
  • 6Alhammady H, Ramanaohanarao K. The Application of Emerging Patterns for Improving the Quality of Rare - class Classification[C]//Proc. of the 8th Pacific- Asia Conf. on Advances in Knowledge Discovery and Data Mining (PAKDD2004). Sydney, Australia: [s. n. ], 2004:207 - 211.
  • 7职为梅,范明.利用基本显露模式两阶段分类稀有类[J].微机发展,2005,15(12):44-47. 被引量：4
  • 8Agarwal R, Joshi M V, Kumar V. Mining Needles in a Haystack: Classifying Rare Classes via Two - Phase Rule Induction[ C]//the Proc of ACM SIGMOD/PODS. [s. l.] : [s. n. ] ,2001:91 - 102.
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二级参考文献15

  • 1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
  • 2HanJ 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..
  • 3Provost 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.
  • 4Agarwal 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).
  • 5Alhammady 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.
  • 6Joshi 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.
  • 7Agarwal R, Joshi M V. PNrule:A new Frameworkfor Learning Classifier Models in Data Mining (A Case- Study in Network Intrusion Detection)[A]. In Proc. of the First SIAM Conference on Data Mining[ C]. Chicago, USA: [ s. n. ], 2001.
  • 8Alhanmady H, Ramamohanarao K. The Application of Emerging Patterns for Improving the Quality of Rare - class Classification[ A]. In Proc. of the 8th Pacific- Asia Conf. on Advances in Knowledge Discovery and Data Mining( PAKDD2004 ) [ C ].Sydney,Australia: [s. n. ] ,2004. 207 - 211.
  • 9Dong G, Li J. Efficient mining of emerging patterns: Discovering trends and differences[ A]. In Proc. of KDD' 99[ C]. San-Diego,USA: [s. n. ], 1999.15 - 18.
  • 10Fan H, Ramamohanarao K. Bayesian Approach to use Emerging Patterns for Classification[A]. In Proc of 14th Australasian Database Conference[C]. Adelaide, Australia: [s. n. ] ,2003.39- 48.

共引文献50

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计算机技术与发展
2010年 第7期

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