MARLINE: Multi-source mapping transfer learning for non-stationary environments

Honghui Du, Leandro L. Minku, Huiyu Zhou

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
EditorsClaudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728183169
Publication statusPublished - 09 Feb 2021
Externally publishedYes
Event20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy
Duration: 17 Nov 202020 Nov 2020

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference20th IEEE International Conference on Data Mining, ICDM 2020
CityVirtual, Sorrento

Bibliographical note

Funding 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).

Publisher Copyright:
© 2020 IEEE.

Copyright 2021 Elsevier B.V., All rights reserved.


  • Concept drifts
  • Multi-sources
  • Non-stationary environment
  • Transfer learning

ASJC Scopus subject areas

  • Engineering(all)


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