Change-point detection is a problem to find change in data. Basically, it assumes that the data is passively given. When the cost of data acquisition is not ignorable, it is desirable to save resources by actively selecting effective data for change-point detection. In this paper, 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.
|ジャーナル||Transactions of the Japanese Society for Artificial Intelligence|
|出版ステータス||出版済み - 2020|
All Science Journal Classification (ASJC) codes