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

Abstract

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.
Pages122-131
Number of pages10
ISBN (Electronic)9781728183169
DOIs
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
Volume2020-November
ISSN (Print)1550-4786

Conference

Conference20th IEEE International Conference on Data Mining, ICDM 2020
CountryItaly
CityVirtual, Sorrento
Period17/11/202020/11/2020

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

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

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