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数据流上概念漂移的检测和分类 被引量：9

Detecting Concept Drift and Classifying Data Streams
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摘要 挖掘带有概念漂移的数据流对于许多实时决策是十分重要的.本文使用统计学理论估计某一确定模型在最新概念上的真实错误率的置信区间,在一定概率保证下检测数据流中是否发生了概念漂移,并将此方法和KMM(核平均匹配)算法引入集成分类器框架中,提出一种数据流分类的新算法WSEC.在仿真和真实数据流上的试验结果表明该算法是有效的. It is very important to mining data streams with concept drifts for many real-time decision support systems. This paper proposed a method to estimate the Confidence Interval of the true error rate of the Up-to-Date concept to a certain model based on the sta- tistical theory. This method could detect the concept drift under a certain probability guarantee. We apply this method and KMM algorithm to the Ensemble Framework of Classifier, and give a new algorithm for data stream classification. The experimental results in the simulation and real data streams show that the algorithm is effective.
作者 柴玉梅 周驰 王黎明
机构地区 郑州大学信息工程学院
出处 《小型微型计算机系统》 CSCD 北大核心 2011年第3期421-425,共5页 Journal of Chinese Computer Systems
基金 河南省自然科学基金项目(2009A520025)资助
关键词 概念漂移 数据流挖掘 分类 集成 concept drift data streams mining classifying ensemble
分类号 TP311 [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15

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二级参考文献12

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共引文献8

同被引文献72

  • 1穆国旺,臧婷,赵罡.用改进遗传算法确定B样条曲线的节点矢量[J].计算机工程与应用,2006,42(11):88-90. 被引量：9
  • 2赵辉,王黎明.一个基于网格服务的分布式关联规则挖掘算法[J].小型微型计算机系统,2006,27(8):1544-1548. 被引量：9
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引证文献9

二级引证文献20

小型微型计算机系统
2011年 第3期

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