DSPOTE: Density-induced Selection Probability-based Oversampling TEchnique for Imbalanced Learning

Abstract

In imbalanced learning, oversampling is incredibly prevalent. However, it is disappointing that existing oversampling methods have their own limitations, such as new synthetic samples may be uninformative or noisy. To better address imbalanced learning tasks, this paper proposes a novel over-sampling method, named Density-induced Selection Probability-based Oversampling TEchnique (DSPOTE). To increase the number of samples in the minority class, DSPOTE designs a novel scheme for filtering noisy samples based on the Chebychev distance and a new way of calculating selection probability based on relative density. DSPOTE first filters noisy samples and then gets borderline ones from the minority class. Next, DSPOTE calculates the selection probabilities for all borderline samples and applies these probabilities to pick up borderline samples. Finally, DSPOTE generates synthetic samples for the minority class based on the selected borderline ones. Experimental results indicate that our method has good performance in terms of metrics, recall and AUC (Area Under the Curve), when compared with other eight methods.