Ensembles of detectors for online detection of transient changes

Alexey Artemov, Evgeny Burnaev

Результат исследований: Глава в книге, отчете, сборнике статейМатериалы для конференциирецензирование

20 Цитирования (Scopus)

Аннотация

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.

Язык оригиналаАнглийский
Название основной публикацииEighth International Conference on Machine Vision, ICMV 2015
РедакторыAntanas Verikas, Petia Radeva, Dmitry Nikolaev
ИздательSPIE
Том9875
ISBN (электронное издание)9781510601161
DOI
СостояниеОпубликовано - 2015
Опубликовано для внешнего пользованияДа
Событие8th International Conference on Machine Vision, ICMV 2015 - Barcelona, Испания
Продолжительность: 19 нояб. 201521 нояб. 2015

Серия публикаций

НазваниеProceedings of SPIE - The International Society for Optical Engineering
Том9875
ISSN (печатное издание)0277-786X
ISSN (электронное издание)1996-756X

Конференция

Конференция8th International Conference on Machine Vision, ICMV 2015
Страна/TерриторияИспания
ГородBarcelona
Период19/11/1521/11/15

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