An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification

arXiv:2405.01557v1 Announce Type: new
Abstract: Predictive models may generate biased predictions when classifying imbalanced datasets. This happens when the model favors the majority class, leading to low performance in accurately predicting the minority class. To address this issue, balancing or resampling methods are critical pre-processing steps in the modeling process. However, there have been debates and questioning of the functionality of these methods in recent years. In particular, many candidate models may exhibit very similar predictive performance, which is called …

abstract arxiv class classification cs.lg datasets experimental generate issue low performance predictions predictive predictive models pre-processing processing resampling study type