非平衡数据集的改进SMOTE再抽样算法

非平衡数据集的不均衡学习特点通常表现为负类的分类效果不理想。改进SMOTE再抽样算法，将过抽样和欠抽样方式有机结合，有针对性地选择近邻并采用不同策略合成样本。实验表明，分类器在经此算法处理后的非平衡数据集的正负两类上，均可获得较理想的分类效果。

An Improved SMOTE Algorithm for Re-Sampling Imbalanced Data Sets

The inharmonious status on training the imbalanced data sets usually show the bad performance on classifying the negative class.By the combination of the over-sampling and under-sampling approaches,The Re-Sampling method based on SMOTE algorithm could control the synthesis of samples choosing the different nearest-neighbors as well as the different strategies.Our experiments show that,general classifier could get relative good results both on positive and negative class after processing by this algorithm.