Pruning and undersampling combination of imbalanced data classification method
剪枝与欠采样相结合的不平衡数据分类方法*

This paper proposed pruning and under-sampling combined approaches for selected the representative data as training data to improve the classification accuracy for minority class and investigated the effect of under-sampling methods in the imbalanced class distribution environment. The experimental results show that the accuracy of algorithm of this paper compare with direct undersampling algorithm have increased, the most important is to significantly improve the g-means value. Especially, the effect will be better on the imbalance rate of larger data sets.