Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift

TL;DR

Lite-RVFL is a fast, lightweight neural network designed for online learning that adapts to concept drift without retraining by emphasizing recent data, suitable for real-time applications.

Contribution

It introduces a novel objective function and incremental update rule enabling drift adaptation without explicit drift detection or retraining.

Findings

Efficiently adapts to concept drift in real-time

Outperforms existing methods in accuracy and speed

Captures temporal patterns effectively

Abstract

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.