In many real-world applications, data are continuously accumulated in open environments, and new classes may emerge over time. For instance, in disease diagnosis, the prevalence of a certain disease may vary seasonally, and new diseases can also emerge. This paper investigates the problem of learning from unlabeled data stream where thelabel distribution evolves over time, and meanwhile,previously unseen new classes may appear. To handle the emerging new classes in online label shift, we first design a novel risk estimator by unbiased risk rewriting and mixture proportion estimation, which enables the identification of new class data. Subsequently, we employ the online ensemble paradigm for model updating to handle unknown distribution shifts. Moreover, we introduce the sketching and ensemble pruning mechanisms to improve the efficiency of the algorithm, making it more lightweight and practical. The proposed approach enjoys a theoretical guarantee of dynamic regret, ensuring its effectiveness in adapting to the unknown distribution shifts and the emergence of new classes in streaming data. Experiments on diverse benchmark datasets and two real-world applications demonstrate the effectiveness of the algorithm.

Learning with data streams has attracted much attention in recent decades. Conventional approaches typically assume that the feature and label of a data item can be timely observed at each round. In many real-world tasks, however, it often occurs that either the feature or the label is observed firstly while the other arrives with delay. For instance, in distributed learning systems, a central processor collects training data from different sub-processors to train a learning model, whereas the feature and label of certain data items can arrive asynchronously due to network latency. The problem of learning with asynchronous feature or label in streams encompasses many applications but still lacks sound solutions. In this article, we formulate the problem and propose a new approach to alleviate the negative effect of asynchronicity and mining asynchronous data streams. Our approach carefully exploits the timely arrived information and builds an online ensemble structure to adaptively reuse historical models and instances. We provide the theoretical guarantees of our approach and conduct extensive experiments to validate its effectiveness.

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.

In plenty of real-world applications, data are collected in a streaming fashion, and their accurate labels are hard to obtain. For instance, in the environmental monitoring task, sensors are collecting the data all the time. Still, their labels are scarce because the labeling process requires human effort and can conceal annotation errors. This paper investigates the problem of learning with weakly labeled data streams, in which data are continuously collected, and only a limited subset of streaming data is labeled but potentially with noise. This setting is challenging and of great importance but rarely studied in the literature. When the data are constantly gathered with unknown noise on labels, it is quite challenging to design algorithms to obtain a well-generalized classifier. To address this difficulty, we propose a novel noise transition matrix estimation approach for data streams with scarce noisy labels by online anchor points identification. Based on that, we propose an adaptive learning algorithm for weakly labeled data streams via model reuse and effectively alleviate the negative influence of label noise with unlabeled data. Both theoretical analysis and extensive experiments justify and validate the effectiveness of the proposed approach.