  Real-world classification domains, such as medicine, health and safety, and
finance, often exhibit imbalanced class priors and have asynchronous
misclassification costs. In such cases, the classification model must achieve a
high recall without significantly impacting precision. Resampling the training
data is the standard approach to improving classification performance on
imbalanced binary data. However, the state-of-the-art methods ignore the local
joint distribution of the data or correct it as a post-processing step. This
can causes sub-optimal shifts in the training distribution, particularly when
the target data distribution is complex. In this paper, we propose Radial-Based
Combined Cleaning and Resampling (RB-CCR). RB-CCR utilizes the concept of class
potential to refine the energy-based resampling approach of CCR. In particular,
RB-CCR exploits the class potential to accurately locate sub-regions of the
data-space for synthetic oversampling. The category sub-region for oversampling
can be specified as an input parameter to meet domain-specific needs or be
automatically selected via cross-validation. Our $5\times2$ cross-validated
results on 57 benchmark binary datasets with 9 classifiers show that RB-CCR
achieves a better precision-recall trade-off than CCR and generally
out-performs the state-of-the-art resampling methods in terms of AUC and
G-mean.
