Multi-Label Transfer Learning in Non-Stationary Data Streams
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914030834155520 |
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| author | Du, Honghui Minku, Leandro Lawlor, Aonghus Zhou, Huiyu |
| author_facet | Du, Honghui Minku, Leandro Lawlor, Aonghus Zhou, Huiyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08181 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Label Transfer Learning in Non-Stationary Data Streams Du, Honghui Minku, Leandro Lawlor, Aonghus Zhou, Huiyu Machine Learning Artificial Intelligence 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. |
| title | Multi-Label Transfer Learning in Non-Stationary Data Streams |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.08181 |