Multi-Label Transfer Learning in Non-Stationary Data Streams

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. T o 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. Index T erms--Concept drift, non-stationary environment, multi-source, multi-label, class imbalance, transfer learning. Most research on data stream learning concentrates on streams with single labels [1]. However, many practical data streaming applications naturally adopt a multi-label paradigm, where each incoming data example has more than one label [2]. For example, a social media post could be tagged with several descriptors, or a movie might be classified under various predefined genres (e.g., Action, Crime, Historical), with each tag or genre representing a unique label.