Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift
To reproduce the experiments with default settings, simply run:
python main.py
If you find this work useful for your research, please consider citing our paper:
@article{hu2025lite,
title={Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift},
author={Hu, Songqiao and Liu, Zeyi and He, Xiao},
journal={arXiv preprint arXiv:2506.08063},
year={2025}
}
@INPROCEEDINGS{11268084,
author={Hu, Songqiao and Liu, Zeyi and He, Xiao},
booktitle={2025 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS)},
title={Lite-RVFL: A Lightweight Random Vector Functional-Link Neural Network for Learning Under Concept Drift},
year={2025},
volume={},
number={},
pages={1-6},
keywords={Adaptation models;Computational modeling;Concept drift;Linear programming;Vectors;Real-time systems;Safety;Computational efficiency;Underwater vehicles;Standards;Concept drift;random vector functional-link network;real-time safety assessment},
doi={10.1109/SAFEPROCESS67117.2025.11268084}}