摘要
针对不平衡数据流的分类问题,结合基于权重的集成分类器与抽样技术,本文提出了一种处理不平衡数据流集成分类器模型.理论分析与实验验证表明,该集成分类器具有更低的计算复杂度,更能适应存在概念漂移的不平衡数据流挖掘分类,其整体分类性能优于基于权重的集成分类器模型,能明显提升少数类的分类精度.
Many real world data streams mining applications involve learning from imbalanced data streams,where such applications expect to have a higher predictive accuracy over the minority class,however most classification model assume relatively balanced data streams,they cannot handle imbalanced distribution.In this paper,we propose a novel ensemble classifier framework（IMDWE） for mining concept-drifting data streams with imbalanced distribution by using weighted ensemble classifier framework sampling technique including over-sampling and under-sampling.Our empirical study shows that the IMDWE is superior and have improves both the efficiency in learning the model and the accuracy in performing classification over the minority class.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2010年第1期184-189,共6页
Acta Electronica Sinica
关键词
分类
集成分类器
不平衡数据流
概念漂移
classification ensemble classifier imbalanced data streams concept drift
