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

Samaneh Khoshrou, Mykola Pechenizkiy

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review


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.

Original languageEnglish
Title of host publicationDiscovery Science 22nd International Conference, DS 2019, Proceedings
EditorsPetra Kralj Novak, Sašo Džeroski, Tomislav Šmuc
Number of pages11
ISBN (Print)9783030337773
Publication statusPublished - 1 Jan 2019
Event22nd International Conference on Discovery Science, DS 2019 - Split, Croatia
Duration: 28 Oct 201930 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11828 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Conference on Discovery Science, DS 2019


  • Data streams
  • Ensembles
  • Long-term learning


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