Multi-Label Transfer Learning in Non-Stationary Data Streams
This addresses the challenge of adapting to concept drift in multi-label streaming data for applications like real-time classification, though it appears incremental as it builds on existing transfer learning ideas.
The paper tackled the problem of label drift in multi-label data streams by proposing two transfer learning methods that leverage knowledge between labels, resulting in improved predictive performance over state-of-the-art approaches in non-stationary environments.
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.