<ul><li>Lite-RVFL is a lightweight, fast, and efficient neural network designed to handle concept drift without requiring drift detection or model retraining.</li><li>It introduces a novel objective function that assigns weights exponentially increasing to new samples, allowing timely adaptation to new data.</li><li>The theoretical analysis supports the feasibility of Lite-RVFL's objective function for drift adaptation, and an efficient incremental update rule is derived.</li><li>Experimental results on a safety assessment task demonstrate Lite-RVFL's efficiency, effectiveness in adapting to drift, and ability to capture temporal patterns.</li></ul>

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

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