Ensembles of detectors for online detection of transient changes

Alexey Artemov; Evgeny Burnaev

doi:10.1117/12.2228369

8 December 2015 Ensembles of detectors for online detection of transient changes

Alexey Artemov, Evgeny Burnaev

Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 98751Z (2015) https://doi.org/10.1117/12.2228369
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain

Abstract

Classical change-point detection procedures assume a change-point model to be known and a change consisting in establishing a new observations regime, i.e. the change lasts infinitely long. These modeling assumptions contradicts applied problems statements. Therefore, even theoretically optimal statistics in practice very often fail when detecting transient changes online. In this work in order to overcome limitations of classical change-point detection procedures we consider approaches to constructing ensembles of change-point detectors, i.e. algorithms that use many detectors to reliably identify a change-point. We propose a learning paradigm and specific implementations of ensembles for change detection of short-term (transient) changes in observed time series. We demonstrate by means of numerical experiments that the performance of an ensemble is superior to that of the conventional change-point detection procedures.

Citation Download Citation

Alexey Artemov and Evgeny Burnaev "Ensembles of detectors for online detection of transient changes", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 98751Z (8 December 2015); https://doi.org/10.1117/12.2228369

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available