人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
ISSN-L : 1346-0714
原著論文
能動的変化点検知
林 勝悟河原 吉伸鹿島 久嗣
著者情報
ジャーナル フリー

2020 年 35 巻 5 号 p. E-JA10_1-10

詳細
抄録

Change-point detection is a problem to find change in data. Basically, it assumes that the data is passivelygiven. When the cost of data acquisition is not ignorable, it is desirable to save resources by actively selecting effectivedata for change-point detection. In this paper, we introduce Active Change-Point Detection (ACPD), a novel activelearning 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 ina black-box expensive-to-evaluate function, with as few evaluations as possible. We propose a novel frameworkthat can be generalized for different types of data and change-points, by utilizing an existing change-point detectionmethod to compute change scores and a Bayesian optimization method to determine the next input. We demonstratethe efficiency of our proposed framework in different settings of datasets and change-points, using synthetic data andreal-world data, such as material science data and seafloor depth data.

著者関連情報
© 人工知能学会 2020
前の記事 次の記事
feedback
Top