8 December 2015 Ensembles of detectors for online detection of transient changes
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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.
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Alexey Artemov, 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); doi: 10.1117/12.2228369; https://doi.org/10.1117/12.2228369
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