Active Change-Point Detection

S. Hayashi, Yoshinobu Kawahara, H. Kashima
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引用次数: 4

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.
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主动变更点检测
我们引入了主动变化点检测(ACPD),这是一种新的主动学习问题,用于在数据采集成本昂贵的情况下进行有效的变化点检测。在每一轮ACPD中,任务是自适应地确定下一个输入,以便用尽可能少的评估来检测黑盒中难以评估的函数中的变化点。我们提出了一个新的框架,可以推广到不同类型的数据和变化点,利用现有的变化点检测方法来计算变化分数和贝叶斯优化方法来确定下一个输入。我们使用合成数据和真实世界的数据,如材料科学数据和海底深度数据,证明了我们提出的框架在不同数据集和变化点设置下的效率。
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