DRCW-ASEG: One-versus-One distance-based relative competence weighting with adaptive synthetic example generation for multi-class imbalanced datasets

Multi-class imbalance learning problems suffering from the different distribution of classes occur in many real-world applications. One-versus-One (OVO) decomposition strategy is a common and useful technique used to address multi-class classification problems, which consists in dividing the original multi-class problem into all binary class sub-problems. The effort to reduce the effect of non-competent classifiers has proven to be a useful way of improving the performance in the OVO scheme. However, these approaches might not be effective for imbalance scenarios, since they are based on standard biased learning procedures. On this account, we propose a novel approach named Distance-based Relative Competence Weighting with Adaptive Synthetic Example Generation (DRCW-ASEG), which properly addresses the synergy between imbalance learning and dynamic classifier weighting in OVO scheme. This new proposed algorithm aims to dynamically produce synthetic examples of minority classes in the stage of dynamic weighting process. We develop a thorough experimental study in order to verify the benefits of the proposed algorithm considering different base binary classifiers.