# MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments

arXiv: https://arxiv.org/abs/2509.08176 · HTML: https://mlsanity.com/paper/2509.08176/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Concept Drift Adaptation by Exploiting Historical Knowledge](https://mlsanity.com/paper/1702.03500/) (2017)
- [Multi-Source Transfer Learning for Non-Stationary Environments](https://mlsanity.com/paper/1901.02052/) (2019)

## Abstract (arXiv)

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.