An Ensemble Classifier Framework for Mining Imbalanced Data Streams
Quanyuan Wu · Dianzi xuebao · 2010
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.