Sequential label shift detection in classification data: An application to dengue fever.

ABSTRACT: Classifiers have been developed to help diagnose dengue fever in patients presenting with febrile symptoms. However, classifier predictions often rely on the assumption that new observations come from the same distribution as training data. If the population prevalence of dengue changes, as would happen with a dengue outbreak, it is important to raise an alarm as soon as possible, so that appropriate public health measures can be taken and also so that the classifier can be re-calibrated. In this paper, we consider the problem of detecting such a change in distribution in sequentially-observed, unlabeled classification data. We focus on label shift changes to the distribution, where the class priors shift but the class conditional distributions remain unchanged. We reduce this problem to t

SUBMITTER: Evans C 

PROVIDER: S-EPMC11404796 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature