# Multi-Source Transfer Learning for Non-Stationary Environments

arXiv: https://arxiv.org/abs/1901.02052 · HTML: https://mlsanity.com/paper/1901.02052/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2019.

## Built on by (most cited first, 9 of 9)

- [Online Boosting Adaptive Learning under Concept Drift for Multistream Classification](https://mlsanity.com/paper/2312.10841/) (2023)
- [Automatic Online Multi-Source Domain Adaptation](https://mlsanity.com/paper/2109.01996/) (2021)
- [ACDC: Online Unsupervised Cross-Domain Adaptation](https://mlsanity.com/paper/2110.01326/) (2021)
- [Autonomous Cross Domain Adaptation under Extreme Label Scarcity](https://mlsanity.com/paper/2209.01548/) (2022)
- [Cross-Domain Continual Learning via CLAMP](https://mlsanity.com/paper/2405.07142/) (2024)
- [Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning](https://mlsanity.com/paper/2507.14597/) (2025)
- [MARLINE: Multi-Source Mapping Transfer Learning for Non-Stationary Environments](https://mlsanity.com/paper/2509.08176/) (2025)
- [Multi-Label Transfer Learning in Non-Stationary Data Streams](https://mlsanity.com/paper/2509.08181/) (2025)
- [Autonomous Drift Learning in Data Streams: A Unified Perspective](https://mlsanity.com/paper/2605.01295/) (2026)

## Abstract (arXiv)

In data stream mining, predictive models typically suffer drops in predictive performance due to concept drift. As enough data representing the new concept must be collected for the new concept to be well learnt, the predictive performance of existing models usually takes some time to recover from concept drift. To speed up recovery from concept drift and improve predictive performance in data stream mining, this work proposes a novel approach called Multi-sourcE onLine TrAnsfer learning for Non-statIonary Environments (Melanie). Melanie is the first approach able to transfer knowledge between multiple data streaming sources in non-stationary environments. It creates several sub-classifiers to learn different aspects from different source and target concepts over time. The sub-classifiers that match the current target concept well are identified, and used to compose an ensemble for predicting examples from the target concept. We evaluate Melanie on several synthetic data streams containing different types of concept drift and on real world data streams. The results indicate that Melanie can deal with a variety drifts and improve predictive performance over existing data stream learning algorithms by making use of multiple sources.