A synthetic over-sampling method with minority and majority classes for imbalance problems

Abstract Class imbalance is a substantial challenge in classifying many real-world cases. Synthetic over-sampling methods have been effective to improve the performance of classifiers for imbalance problems. However, most synthetic over-sampling methods generate synthetic instances within the convex hull formed by the existing minority instances as they only concentrate on the minority class and ignore the vast information provided by the majority class. They also often do not perform well for extremely imbalanced data, as fewer minority instances mean less information with which to generate synthetic instances. Moreover, existing methods that generate synthetic instances using the majority class distributional information cannot perform effectively when the majority class has a multi-modal distribution. We propose a new method to generate diverse and adaptable synthetic instances using Synthetic Over-sampling with Minority and Majority classes (SOMM). SOMM generates synthetic instances diversely within the minority data space. It updates the generated instances adaptively to the neighbourhood including both classes. Thus, SOMM performs well for imbalance problems. We examine the performance of SOMM for binary multiclass imbalance classification problems for different imbalance levels. The empirical results and nonparametric statistical testing show the superiority of SOMM compared to existing methods. We also discuss the strengths and limitations of SOMM through visualisations.