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Class imbalance is a substantial challenge in classifying many real-worldcases. Synthetic over-sampling methods have been effective to improve theperformance of classifiers for imbalance problems. However, most syntheticover-sampling methods generate non-diverse synthetic instances within theconvex hull formed by the existing minority instances as they only concentrateon the minority class and ignore the vast information provided by the majorityclass. They also often do not perform well for extremely imbalanced data as thefewer the minority instances, the less information to generate syntheticinstances. Moreover, existing methods that generate synthetic instances usingthe majority class distributional information cannot perform effectively whenthe majority class has a multi-modal distribution. We propose a new method togenerate diverse and adaptable synthetic instances using SyntheticOver-sampling with Minority and Majority classes (SOMM). SOMM generatessynthetic instances diversely within the minority data space. It updates thegenerated instances adaptively to the neighbourhood including both classes.Thus, SOMM performs well for both binary and multiclass imbalance problems. Weexamine the performance of SOMM for binary and multiclass problems usingbenchmark data sets for different imbalance levels. The empirical results showthe superiority of SOMM compared to other existing methods.

A Synthetic Over-sampling method with Minority and Majority classes for imbalance problems