基于改进SMOTE的不平衡数据挖掘方法研究

Research on Datamining Method for Imbalanced Dataset Based on Improved SMOTE

少类样本合成过采样技术（SMOTE）是一种新型的过采样方法，能够有效地处理不平衡数据分类问题，但SMOTE在产生合成样本的过程中，存在一定的盲目性．因此本文提出一种改进的过采样方法一自适应SMOTE，根据样本集内部分布特性，自适应调整SMOTE方法中近邻选择策略，控制合成样本的质量．算法分析和仿真结果表明，文中提出的方法在不影响计算复杂度的前提下，有效地提高了分类算法的整体分类准确率。

Synthetic minority over-sampling technique （SMOTE） is an effective over-sampling technique and can solve the problem of learning from imbalanced dataset. However,in the process of synthetic sample generating, SMOTE is of some blindness. Therefore, a new kind of over-sampling technique-ASMOTE, is proposed. Based on the distribution of the dataset, ASMOTE adjusts the neighbor selective strategy of SMOTE in order to control the quality of the new sample. Through theoretical analysis and empirical study, we show that our method augments the classification accuracy rate effectively without increasing the computation complexity.