MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments
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| Format: | Preprint |
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2025
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| _version_ | 1866912579829366784 |
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| author | Du, Honghui Minku, Leandro Zhou, Huiyu |
| author_facet | Du, Honghui Minku, Leandro Zhou, Huiyu |
| contents | Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data streams from different sources as a strategy to tackle concept drift in a given target domain. These approaches make the assumption that at least one of the source models represents a concept similar to the target concept, which may not hold in many real-world scenarios. In this paper, we propose a novel approach called Multi-source mApping with tRansfer LearnIng for Non-stationary Environments (MARLINE). MARLINE can benefit from knowledge from multiple data sources in non-stationary environments even when source and target concepts do not match. This is achieved by projecting the target concept to the space of each source concept, enabling multiple source sub-classifiers to contribute towards the prediction of the target concept as part of an ensemble. Experiments on several synthetic and real-world datasets show that MARLINE was more accurate than several state-of-the-art data stream learning approaches. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_08176 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments Du, Honghui Minku, Leandro Zhou, Huiyu Machine Learning Artificial Intelligence Concept drift is a major problem in online learning due to its impact on the predictive performance of data stream mining systems. Recent studies have started exploring data streams from different sources as a strategy to tackle concept drift in a given target domain. These approaches make the assumption that at least one of the source models represents a concept similar to the target concept, which may not hold in many real-world scenarios. In this paper, we propose a novel approach called Multi-source mApping with tRansfer LearnIng for Non-stationary Environments (MARLINE). MARLINE can benefit from knowledge from multiple data sources in non-stationary environments even when source and target concepts do not match. This is achieved by projecting the target concept to the space of each source concept, enabling multiple source sub-classifiers to contribute towards the prediction of the target concept as part of an ensemble. Experiments on several synthetic and real-world datasets show that MARLINE was more accurate than several state-of-the-art data stream learning approaches. |
| title | MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.08176 |