轻量级神经网络无需重训练即可实时应对数据分布变化。
Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift
- 用指数加权新样本的损失函数，让模型自动关注最新数据。
- 在真实安全评估任务中实现快速适应，准确率优于传统方法。
- 适合资源受限场景下的在线学习，尤其适合实时系统。
数据分布随时间变化（概念漂移）严重威胁在线学习方法的可靠性。现有方法通常需模型重训练或漂移检测，计算开销大，难以满足实时性要求。为此，本文提出一种轻量、快速、高效的随机向量函数链接网络 Lite-RVFL，可在无需漂移检测和重训练的情况下适应概念漂移。Lite-RVFL 引入新型目标函数，对新样本赋予指数增长的权重，强化近期数据影响，实现及时适应。理论分析证明该函数适用于漂移适应，并推导出高效增量更新规则。在真实世界安全评估任务上的实验验证了 Lite-RVFL 的效率、漂移适应能力及捕捉时序模式的潜力。源代码见 https://github.com/songqiaohu/Lite-RVFL。
原文摘要 · Abstract (English)
The change in data distribution over time, also known as concept drift, poses a significant challenge to the reliability of online learning methods. Existing methods typically require model retraining or drift detection, both of which demand high computational costs and are often unsuitable for real-time applications. To address these limitations, a lightweight, fast and efficient random vector functional-link network termed Lite-RVFL is proposed, capable of adapting to concept drift without drift detection and retraining. Lite-RVFL introduces a novel objective function that assigns weights exponentially increasing to new samples, thereby emphasizing recent data and enabling timely adaptation. Theoretical analysis confirms the feasibility of this objective function for drift adaptation, and an efficient incremental update rule is derived. Experimental results on a real-world safety assessment task validate the efficiency, effectiveness in adapting to drift, and potential to capture temporal patterns of Lite-RVFL. The source code is available at https://github.com/songqiaohu/Lite-RVFL.
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