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 Nonstationary 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 of host publication||Proceedings - 20th IEEE International Conference on Data Mining, ICDM 2020|
|Editors||Claudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|Publication status||Published - 09 Feb 2021|
|Event||20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy|
Duration: 17 Nov 2020 → 20 Nov 2020
|Name||Proceedings - IEEE International Conference on Data Mining, ICDM|
|Conference||20th IEEE International Conference on Data Mining, ICDM 2020|
|Period||17/11/2020 → 20/11/2020|
Bibliographical noteFunding Information:
Authors in order of contribution. L. Minku was supported by EPSRC Grant No. EP/R006660/2 and H. Zhou was supported by EU Horizon 2020 DOMINOES Project (grant number: 771066).
© 2020 IEEE.
Copyright 2021 Elsevier B.V., All rights reserved.
- Concept drifts
- Non-stationary environment
- Transfer learning
ASJC Scopus subject areas