A bias correction function for classification performance assessment in two-class imbalanced problems

摘要

This paper introduces a framework that allows to mitigate the impact of class imbalance on most scalar performance measures when used to evaluate the behavior of classifiers. Formally, a correction function is defined with the aim of highlighting those classification results that present moderately higher prediction rates on the minority class. Besides, this function punishes those scenarios that are biased towards the majority class, but also those that are strongly biased to favor the minority class. This strategy assumes a typical imbalance task, in which the minority class contains the most relevant samples to the research purposes. A novel experimental framework is designed to show the advantages of our approach when compared to the standard use of well-established measures, demonstrating its consistency and validity.