Adaptive Long-Term Ensemble Learning from Multiple High-Dimensional Time-Series

  • Samaneh KhoshrouEmail author
  • Mykola Pechenizkiy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11828)


Learning from multiple time-series over an unbounded time-frame has received less attention despite the key applications (such as video analysis, home-assisted) generating this data. Inspired by never-ending approaches, this paper presents an algorithm to continuously learn from multiple high-dimensional un-regulated time-series, in a framework based on ensembles which with respect to drift level develops over time in order to reflect the latest concepts. Here, we explicitly look into video surveillance problem as one of the main sources of high-dimensional data in daily life and extensive experiments are conducted on multiple datasets, that demonstrate the advantages of the proposed framework in terms of accuracy and complexity over several baseline approaches.


Long-term learning Data streams Ensembles 



The authors would like to thank the Dutch Research Council (NWO) for supporting this project.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands

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