  Data imbalance remains one of the most widespread problems affecting
contemporary machine learning. The negative effect data imbalance can have on
the traditional learning algorithms is most severe in combination with other
dataset difficulty factors, such as small disjuncts, presence of outliers and
insufficient number of training observations. Aforementioned difficulty factors
can also limit the applicability of some of the methods of dealing with data
imbalance, in particular the neighborhood-based oversampling algorithms based
on SMOTE. Radial-Based Oversampling (RBO) was previously proposed to mitigate
some of the limitations of the neighborhood-based methods. In this paper we
examine the possibility of utilizing the concept of mutual class potential,
used to guide the oversampling process in RBO, in the undersampling procedure.
Conducted computational complexity analysis indicates a significantly reduced
time complexity of the proposed Radial-Based Undersampling algorithm, and the
results of the performed experimental study indicate its usefulness, especially
on difficult datasets.
