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.