A Novel One-Class Classification Framework for Highly Imbalanced Binary Outcomes: the OC-Cat Approach (Epidemiology, Biostatistics, and Public Health, 2025)
IntroductionExtremely rare events can challenge traditional classification models, which may exhibit reduced power in highly unbalanced datasets (i.e., when two or more target groups are unevenly represented). Moreover, this effect seems to be accentuated by the reduction of the sample size. Some of the easiest and intuitive methods proposed to handle unbalanced datasets, while still using a classical statistical models, are random under- or oversampling or hybrid methods[1]. Alternatively, other approaches have been proposed with different strategies, such as ensemble models (e.g. AdaBoost, XGBoost), or novelty detection models[2].In medicine, this kind of scenario can occur when analysing catheter related/associated blood stream infections (CRBSI/CABSI), whose incidence usually remains