Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes
This addresses the challenge of efficient resource allocation in change-point detection for domains like decision-making, though it appears incremental as it builds on existing active learning methods.
The paper tackles the problem of costly data acquisition in change-point detection by introducing the Derivative-Aware Change Detection (DACD) method, which uses derivative-aware Gaussian processes for active learning and shows it outperforms other approaches in diverse scenarios.
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.