  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 non-diverse 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 the
fewer the minority instances, the less information 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 both binary and multiclass imbalance problems. We
examine the performance of SOMM for binary and multiclass problems using
benchmark data sets for different imbalance levels. The empirical results show
the superiority of SOMM compared to other existing methods.
