能動的変化点検知

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.