Handling New Class in Online Label Shift

In many real-world applications, data are continuously accumulated within open environments. For instance, in disease diagnosis, the prevalence of diseases can vary across seasons, and new types of diseases can emerge. This paper investigates the problem of learning from unlabeled data where the label distribution evolves over time, and meanwhile, previously unseen new class appears in the data stream. To handle the new class in online label shift, we first design a novel risk estimator by unbiased risk rewriting and mixture proportion estimation. Subsequently, we employ the online ensemble paradigm for model updating to handle unknown distribution shifts. The proposed approach enjoys a theoretical guarantee of dynamic regret, ensuring its effectiveness in adapting to the changing label distribution and the presence of the new class in streams. Experiments conducted on diverse benchmark datasets and two real-world applications demonstrate the effectiveness of the proposed algorithm.