Change-point detection (CPD) is crucial for identifying abrupt shifts in data, which influence decision-making and efficient resource allocation across various domains. To address the challenges posed by the costly and time-intensive data acquisition in CPD, we introduce the Derivative-Aware Change Detection (DACD) method. It leverages the derivative process of a Gaussian process (GP) for Active Learning (AL), aiming to pinpoint change-point locations effectively. DACD balances the exploitation and exploration of derivative processes through multiple data acquisition functions (AFs). By utilizing GP derivative mean and variance as criteria, DACD sequentially selects the next sampling data point, thus enhancing algorithmic efficiency and ensuring reliable and accurate results. We investigate the effectiveness of DACD method in diverse scenarios and show it outperforms other active learning change-point detection approaches.

引入Derivative-Aware Change Detection (DACD)方法，通过使用高斯过程（GP）的导数过程进行主动学习（AL），以有效确定变点位置。DACD通过多个数据采集函数（AFs）平衡导数过程的开发和探索，并利用GP导数均值和方差作为准则，选择下一个采样数据点，从而提高算法效率和确保可靠准确的结果。研究表明DACD方法在不同场景下的有效性，并优于其他主动学习变点检测方法。

基于导数感知高斯过程的突变点检测的主动学习