通过迁移标签间关系，提升非平稳数据流的多标签分类效果。
Multi-Label Transfer Learning in Non-Stationary Data Streams
- 利用源与目标流中不同标签的知识进行跨标签迁移
- 显式建模标签对依赖关系，显著提升分类准确率
- 适合处理动态变化的多标签数据流场景
多标签数据流中的标签概念在非平稳环境中常发生漂移，可能独立或相互关联。跨标签知识迁移可加速模型适应，但针对数据流的多标签迁移学习研究仍有限。为此，我们提出两种新方法：BR-MARLENE 利用源流与目标流中不同标签的知识进行多标签分类；BRPW-MARLENE 在此基础上显式建模并迁移标签对之间的依赖关系，以提升学习性能。大量实验表明，两种方法在非平稳环境下的多标签流任务中均优于现有先进方法，验证了标签间知识迁移的有效性，显著提升了预测性能。
原文摘要 · Abstract (English)
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.
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