基于多分类器集成的数据流分类方法
DOI:
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

TP181

基金项目:


Ensemble Classifier Based Data Stream Classifying
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    概念漂移给数据流挖掘工作带来了很大阻碍。经典的SEA算法通过动态裁剪集成分类器的方式有效地捕获到概念漂移。其裁剪集成分类器的策略是直接删除掉一个权值最低的基础分类器,这意味着算法抛弃了一个已经学习了的概念,当该概念再出现时还需再学习,导致算法效率的降低。本文提出了一种能够提取旧概念的算法ECRRC,并给出了存储和提取概念的具体方法。面对概念的重复出现,ECRRC不用再学习就能够完成数据流分类。实验结果表明,ECRRC能够提高数据流分类效率。

    Abstract:

    Concept drift is a big obstacle in the field of mining stream data. By dynamic modifying the ensemble classifier, SEA can effectively catch concept drift for mining stream data. The method of SEA modifying the ensemble classifier is direct dropping a base classifier of the lowest weight. That means the algorithm abandon a learned concept, but the algorithm will waste time to learn the abandoned concept, as a result this leads to a low-level effective algorithm. In this paper, a new algorithm ECRRC(Ensemble Classifiers Retrieving Repeated Concept ) with the ability of retrieving the old concept is proposed to reuse the old classifier. Facing the concept repeating, ECRRC need not learn again for mining stream data. Besides the method of storing and retrieving the concept is presented in this paper. The experimental results show that the algorithm in this paper raises classifying data stream efficiency.

    参考文献
    相似文献
    引证文献
引用本文

王锡文,贾银山. 基于多分类器集成的数据流分类方法[J]. 科学技术与工程, 2010, (18): .
wangxiwen, jiayinshan. Ensemble Classifier Based Data Stream Classifying[J]. Science Technology and Engineering,2010,(18).

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2010-04-02
  • 最后修改日期:2010-04-02
  • 录用日期:2010-04-12
  • 在线发布日期: 2010-05-21
  • 出版日期:
×
律回春渐,新元肇启|《科学技术与工程》编辑部恭祝新岁!
亟待确认版面费归属稿件,敬请作者关注