  Data imbalance remains one of the factors negatively affecting the
performance of contemporary machine learning algorithms. One of the most common
approaches to reducing the negative impact of data imbalance is preprocessing
the original dataset with data-level strategies. In this paper we propose a
unified framework for imbalanced data over- and undersampling. The proposed
approach utilizes radial basis functions to preserve the original shape of the
underlying class distributions during the resampling process. This is done by
optimizing the positions of generated synthetic observations with respect to
the potential resemblance loss. The final Potential Anchoring algorithm
combines over- and undersampling within the proposed framework. The results of
the experiments conducted on 60 imbalanced datasets show outperformance of
Potential Anchoring over state-of-the-art resampling algorithms, including
previously proposed methods that utilize radial basis functions to model class
potential. Furthermore, the results of the analysis based on the proposed data
complexity index show that Potential Anchoring is particularly well suited for
handling naturally complex (i.e. not affected by the presence of noise)
datasets.
