BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints

A. Maslov, M. Pechenizkiy, Y. Pei, I. Zliobaite, A. Shklyaev, T. Karkkainen, J. Hollmen

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

2 Citaten (Scopus)

Samenvatting

Online changepoint detection is an important task for machine learning in changing environments, as it signals when the learning model needs to be updated. Presence of noise that can be mistaken for real changes makes it difficult to develop an effective approach that would have a low false alarm rate and being able to detect all the changes with a minimal delay. In this paper we study how performance of popular Bayesian online detectors can be improved in case of recurrent changes. Modelling recurrence allows us to anticipate future changepoints and predict their locations in time. We propose an approach for inducing and integrating recurrence information in the streaming settings, and demonstrate its effectiveness on synthetic and real-world human activity datasets.

Originele taal-2Engels
Titel2017 International Joint Conference on Neural Networks (IJCNN), 14-19 May 2017, Anchorage, Arkansas
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's1916-1923
Aantal pagina's8
ISBN van elektronische versie978-1-5090-6182-2
ISBN van geprinte versie978-1-5090-6183-9
DOI's
StatusGepubliceerd - 30 jun 2017
Evenement2017 International Joint Conference on Neural Networks (IJCNN 2017) - William A. Egan Civic and Convention Center, Anchorage, Verenigde Staten van Amerika
Duur: 14 mei 201719 mei 2017

Congres

Congres2017 International Joint Conference on Neural Networks (IJCNN 2017)
Verkorte titelIJCNN 2017
Land/RegioVerenigde Staten van Amerika
StadAnchorage
Periode14/05/1719/05/17

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