Abstract
Label concepts in multi-label data streams often experience drift in non-stationary environments, either independently or in relation to other labels. Transferring knowledge between related labels can accelerate adaptation, yet research on multi-label transfer learning for data streams remains limited. To address this, we propose two novel transfer learning methods: BR-MARLENE leverages knowledge from different labels in both source and target streams for multi-label classification; BRPW-MARLENE builds on this by explicitly modelling and transferring pairwise label dependencies to enhance learning performance. Comprehensive experiments show that both methods outperform state-of-the-art multi-label stream approaches in non-stationary environments, demonstrating the effectiveness of inter-label knowledge transfer for improved predictive performance.
Abstract (translated)
在多标签数据流中，标签概念往往会在非平稳环境中经历漂移，这种漂移可能是独立发生的，也可能是与其他标签相关的。利用相关标签之间的知识转移可以加速适应过程，但针对数据流的多标签迁移学习研究仍然有限。为了解决这一问题，我们提出了两种新颖的迁移学习方法：BR-MARLENE 方法从源和目标数据流中的不同标签中借用知识进行多标签分类；而 BRPW-MARLENE 则在此基础上进一步显式地建模并转移成对标签依赖性，以增强学习性能。全面的实验表明，在非平稳环境中，这两种方法均优于最先进的多标签流处理方法，证明了标签间知识迁移在提高预测性能方面的有效性。
URL
https://arxiv.org/abs/2509.08181
