Active Change-Point Detection

Shogo Hayashi, Yoshinobu Kawahara, Hisashi Kashima
; Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:1017-1032, 2019.

Abstract

We introduce Active Change-Point Detection (ACPD), a novel active learning problem for efficient change-point detection in situations where the cost of data acquisition is expensive. At each round of ACPD, the task is to adaptively determine the next input, in order to detect the change-point in a black-box expensive-to-evaluate function, with as few evaluations as possible. We propose a novel framework that can be generalized for different types of data and change-points, by utilizing an existing change-point detection method to compute change scores and a Bayesian optimization method to determine the next input. We demonstrate the efficiency of our proposed framework in different settings of datasets and change-points, using synthetic data and real-world data, such as material science data and seafloor depth data.

Cite this Paper


BibTeX
@InProceedings{pmlr-v101-hayashi19a, title = {Active Change-Point Detection}, author = {Hayashi, Shogo and Kawahara, Yoshinobu and Kashima, Hisashi}, booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning}, pages = {1017--1032}, year = {2019}, editor = {Wee Sun Lee and Taiji Suzuki}, volume = {101}, series = {Proceedings of Machine Learning Research}, address = {Nagoya, Japan}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/hayashi19a/hayashi19a.pdf}, url = {http://proceedings.mlr.press/v101/hayashi19a.html}, abstract = {We introduce Active Change-Point Detection (ACPD), a novel active learning problem for efficient change-point detection in situations where the cost of data acquisition is expensive. At each round of ACPD, the task is to adaptively determine the next input, in order to detect the change-point in a black-box expensive-to-evaluate function, with as few evaluations as possible. We propose a novel framework that can be generalized for different types of data and change-points, by utilizing an existing change-point detection method to compute change scores and a Bayesian optimization method to determine the next input. We demonstrate the efficiency of our proposed framework in different settings of datasets and change-points, using synthetic data and real-world data, such as material science data and seafloor depth data.} }
Endnote
%0 Conference Paper %T Active Change-Point Detection %A Shogo Hayashi %A Yoshinobu Kawahara %A Hisashi Kashima %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-hayashi19a %I PMLR %J Proceedings of Machine Learning Research %P 1017--1032 %U http://proceedings.mlr.press %V 101 %W PMLR %X We introduce Active Change-Point Detection (ACPD), a novel active learning problem for efficient change-point detection in situations where the cost of data acquisition is expensive. At each round of ACPD, the task is to adaptively determine the next input, in order to detect the change-point in a black-box expensive-to-evaluate function, with as few evaluations as possible. We propose a novel framework that can be generalized for different types of data and change-points, by utilizing an existing change-point detection method to compute change scores and a Bayesian optimization method to determine the next input. We demonstrate the efficiency of our proposed framework in different settings of datasets and change-points, using synthetic data and real-world data, such as material science data and seafloor depth data.
APA
Hayashi, S., Kawahara, Y. & Kashima, H.. (2019). Active Change-Point Detection. Proceedings of The Eleventh Asian Conference on Machine Learning, in PMLR 101:1017-1032

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