SP-SMOTE: A novel space partitioning based synthetic minority oversampling technique

摘要

Traditional machine learning algorithms are always trapped by the class-imbalance problem due to they are biased to the majority class. As one of the most efficient techniques to solve the class-imbalance problem, oversampling technique has attracted many researchers’ attention. An obvious observation of an imbalanced dataset is that there is a clear density difference between minority class and majority class. In view of this, we propose a new density-adaptive space partition method called Dannoy. It can distinguish minority class from dataset easily. After that, a novel space partitioning based synthetic minority oversampling technique named SP-SMOTE is also proposed in this paper to deal with the class imbalance problem. Experiments on four synthetic and fifteen real-world datasets are performed and the results on all real-world datasets demonstrate that the average performances (Accuracy, F1-measure, G-mean and AUC) of SP-SMOTE is superior to the other existing popular algorithms SMOTE, ADASYN, K-means SMOTE, Borderline-SMOTE (1 & 2), polynom_fit_SMOTE and ProWSyn.