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We propose performing imbalanced classification by regrouping majorityclasses into small classes so that we turn the problem into balanced multiclassclassification. This new idea is dramatically different from popular lossreweighting and class resampling methods. Our preliminary result on imbalancedmedical image classification shows that this natural idea can substantiallyboost the classification performance as measured by average precision(approximately area-under-the-precision-recall-curve, or AUPRC), which is moreappropriate for evaluating imbalanced classification than other metrics such asbalanced accuracy.

Imbalanced Classification in Medical Imaging via Regrouping