A Multiscale Concept Drift Detection Method for Learning from Data Streams

Xuesong Wang, Qi Kang, Mengchu Zhou, Siya Yao

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

8 Scopus citations

Abstract

Concept drifts can occur in data streams, which disable any models built on static data distribution to fit dynamic or recurrent concepts. If there is a feature that can monitor the stableness of such distribution, then we have a proper reference to adapt the model. Based on this idea, we propose a novel approach named Multiscale Drift Detection Test (MDDT) that localizes abrupt drift points when detection feature values fluctuate. MDDT is based on a resampling scheme and a paired student t-test. It applies a detection procedure on a broad and a narrow scale. This multiscale structure not only reduces massive time of a constant checking process, but also filters noise in the detection features. Experiments are performed via synthetic and real-world datasets. The results indicate that the proposed method outperforms the state-of-art algorithms in terms of computation cost and average accuracy.

Original languageEnglish (US)
Title of host publication2018 IEEE 14th International Conference on Automation Science and Engineering, CASE 2018
PublisherIEEE Computer Society
Pages786-790
Number of pages5
ISBN (Electronic)9781538635933
DOIs
StatePublished - Dec 4 2018
Event14th IEEE International Conference on Automation Science and Engineering, CASE 2018 - Munich, Germany
Duration: Aug 20 2018Aug 24 2018

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2018-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other14th IEEE International Conference on Automation Science and Engineering, CASE 2018
Country/TerritoryGermany
CityMunich
Period8/20/188/24/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Concept drift detection
  • data stream
  • learning algorithm
  • optimization
  • undersampling

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