Multistream classification poses significant challenges due to the necessity for rapid adaptation in dynamic streaming processes with concept drift. Despite the growing research outcomes in this area, there has been a notable oversight regarding the temporal dynamic relationships between these streams, leading to the issue of negative transfer arising from irrelevant data. In this paper, we propose a novel Online Boosting Adaptive Learning (OBAL) method that effectively addresses this limitation by adaptively learning the dynamic correlation among different streams. Specifically, OBAL operates in a dual-phase mechanism, in the first of which we design an Adaptive COvariate Shift Adaptation (AdaCOSA) algorithm to construct an initialized ensemble model using archived data from various source streams, thus mitigating the covariate shift while learning the dynamic correlations via an adaptive re-weighting strategy. During the online process, we employ a Gaussian Mixture Model-based weighting mechanism, which is seamlessly integrated with the acquired correlations via AdaCOSA to effectively handle asynchronous drift. This approach significantly improves the predictive performance and stability of the target stream. We conduct comprehensive experiments on several synthetic and real-world data streams, encompassing various drifting scenarios and types. The results clearly demonstrate that OBAL achieves remarkable advancements in addressing multistream classification problems by effectively leveraging positive knowledge derived from multiple sources.

翻译：多流分类面临重大挑战，由于需在存在概念漂移的动态流式过程中实现快速适应。尽管该领域研究成果日益增多，但关于各数据流之间时间动态关系的研究明显不足，导致不相关数据引发的负迁移问题。本文提出一种新型在线Boosting自适应学习方法（OBAL），通过自适应学习不同数据流间的动态相关性有效解决了上述局限。具体而言，OBAL采用双阶段机制：第一阶段设计自适应协变量偏移调整算法（AdaCOSA），利用各源数据流的存档数据构建初始集成模型，通过自适应重加权策略在学习动态相关性的同时缓解协变量偏移；在线处理阶段采用基于高斯混合模型的加权机制，该机制与AdaCOSA获取的相关性无缝集成以有效处理异步漂移。该方法显著提升了目标数据流的预测性能与稳定性。我们在涵盖多种漂移场景与类型的合成及真实数据流上开展了全面实验，结果证明OBAL通过有效利用多源正知识，在处理多流分类问题上取得显著进展。