一种不平衡数据流集成分类模型

 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.

An Ensemble Classifier Framework for Mining Imbalanced Data Streams

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 i...