[2604.01651] Label Shift Estimation With Incremental Prior Update

Computer Science > Machine Learning arXiv:2604.01651 (cs) [Submitted on 2 Apr 2026] Title:Label Shift Estimation With Incremental Prior Update Authors:Yunrui Zhang, Gustavo Batista, Salil S. Kanhere View a PDF of the paper titled Label Shift Estimation With Incremental Prior Update, by Yunrui Zhang and 2 other authors View PDF HTML (experimental) Abstract:An assumption often made in supervised learning is that the training and testing sets have the same label distribution. However, in real-life scenarios, this assumption rarely holds. For example, medical diagnosis result distributions change over time and across locations; fraud detection models must adapt as patterns of fraudulent activity shift; the category distribution of social media posts changes based on trending topics and user demographics. In the task of label shift estimation, the goal is to estimate the changing label distribution $p_t(y)$ in the testing set, assuming the likelihood $p(x|y)$ does not change, implying no concept drift. In this paper, we propose a new approach for post-hoc label shift estimation, unlike previous methods that perform moment matching with confusion matrix estimated from a validation set or maximize the likelihood of the new data with an expectation-maximization algorithm. We aim to incrementally update the prior on each sample, adjusting each posterior for more accurate label shift estimation. The proposed method is based on intuitive assumptions on classifiers that are generally ...