基于未学习的滑动窗口在概念漂移下的持续学习
Unlearning-based sliding window for continual learning under concept drift
AI总结：
本文提出一种基于机器未学习的滑动窗口方法，用于解决概念漂移下的持续学习问题，通过有效遗忘旧数据并更新模型，提高计算效率。
摘要：
传统机器学习假设数据分布稳定，但许多现实应用处理的是非稳态数据流，其中底层概念随时间变化。此问题也可视为无任务持续学习中的概念漂移，模型需连续适应而无需显式任务身份或边界。有效学习需要快速适应新数据并遗忘旧信息。常用解决方案基于滑动窗口，但此方法计算开销大，因为模型需反复从头重新训练最新数据。本文提出基于机器未学习的不同视角：当活跃窗口变化时，不重新构建模型，而是通过未学习移除旧样本的影响，然后用新观测数据更新模型。这实现了高效、针对性的遗忘，同时保持对演变分布的适应。据我们所知，这是首次将机器未学习与概念漂移缓解结合用于无任务持续学习。在多个漂移场景下的图像流分类实验中，所提方法在竞争性和计算效率上优于标准滑动窗口重训练。我们的实现可在https://anonymous.4open.science/r/MUNDataStream-60F3找到。
英文摘要：
Traditional machine learning assumes a stationary data distribution, yet many real-world applications operate on nonstationary streams in which the underlying concept evolves over time. This problem can also be viewed as task-free continual learning under concept drift, where a model must adapt sequentially without explicit task identities or task boundaries. In such settings, effective learning requires both rapid adaptation to new data and forgetting of outdated information. A common solution is based on a sliding window, but this approach is often computationally demanding because the model must be repeatedly retrained from scratch on the most recent data. We propose a different perspective based on machine unlearning. Instead of rebuilding the model each time the active window changes, we remove the influence of outdated samples using unlearning and then update the model with newly observed data. This enables efficient, targeted forgetting while preserving adaptation to evolving distributions. To the best of our knowledge, this is the first work to connect machine unlearning with concept drift mitigation for task-free continual learning. Empirical results on image stream classification across multiple drift scenarios demonstrate that the proposed approach offers a competitive and computationally efficient alternative to standard sliding-window retraining. Our implementation can be found at \hrehttps://anonymous.4open.science/r/MUNDataStream-60F3}{https://anonymous.4open.science/r/MUNDataStream-60F3}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
