Abstract
In this paper we propose Radial-Based Combined Cleaning and Resampling (RB-CCR) algorithm. RB-CCR utilizes the concept of class potential to refine the energy-based resampling approach of previously proposed CCR algorithm. 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. The results of the conducted experimental study show that RB-CCR achieves a better precision-recall trade-off than CCR and generally outperforms the state-of-the-art resampling methods in terms of AUC and G-mean.
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