基于三支决策的不平衡数据过采样方法

采样是解决不平衡数据分类问题的一个有效途径.文中结合三支决策理论，根据样本分布将样本划分成三个区域：正域、边界域和负域；在此基础上，分别对边界域和负域中的小类样本进行不同的过采样处理，提出了一种基于三支决策的不平衡数据过采样算法（TWD-IDOS算法）.实验结果表明，在C4.5、KNN和CART等分类器上，文中提出的算法能有效解决不平衡数据的二分类问题，在Recall、F-value、AUC等指标上优于文献中的过采样算法.

An Oversampling Method for Imbalance Data Based on Three-Way Decision Model

Sampling is an effective way to solve the problem of unbalanced data classification.According to the distribution of samples,we employ the three-way decision model to divide the universe into three parts:positive region,boundary region and negative region.After that,we oversample the minority class samples in boundary region and negative region respectively.Then,a novel oversampling algorithm for imbalance data based on three-way decision model,namely TWD-IDOS,is developed.The experimental results show that the proposed method can effectively solve the two-class classification problems of imbalanced data and has a better performance in such measures (Recall、F-value、AUC) on C45,KNN and CART classifiers than other oversampling methods.