Ensemble Online Sequential Extreme Learning Machine for Air Quality Prediction

Liu，Ye1; Cao，Weipeng1; Liu，Yiwen2; Li，Dachuan3; Wang，Qiang3

10.1109/ICCSSE52761.2021.9545089

2021-07-30

978-1-6654-4406-4

2021 7th International Conference on Control Science and Systems Engineering, ICCSSE 2021

233-237

30 July-1 Aug. 2021

Qingdao, China

Online Sequential Extreme Learning Machine (OS-ELM) has been confirmed by numerous studies to be an effective algorithm for online learning scenarios. However, we found that some parameters of OS-ELM are randomly assigned and remain unchanged in the subsequent learning process, which leads to great instability in the model performance in practice. To alleviate this problem, we propose a novel ensemble OS-ELM algorithm (EOS-ELM-R) for solving air quality prediction problems. EOS-ELM-R uses multiple distribution functions to initialize the random parameters of the base OS-ELM models and its final output is the average of the predictions of these base models. Extensive experimental results on two real-world air quality prediction problems show that EOS-ELM-R is effective, and it can achieve better generalization capabilities than similar algorithms.

air quality prediction ensemble learning extreme learning machine Online learning online sequential extreme learning machine

其他

英语

EI

20214110999862

Air quality ; E-learning ; Forecasting ; Knowledge acquisition ; Machine learning

Air Pollution Control:451.2 ; Artificial Intelligence:723.4 ; Probability Theory:922.1

2-s2.0-85116539116

Scopus

1.Shenzhen University,College of Computer Science and Software Engineering,Shenzhen,China
2.Shenzhen University,College of International Exchange,Shenzhen,China
3.Southern University of Science and Technology,Department of Computer Science and Engineering,Shenzhen,China

Liu，Ye,Cao，Weipeng,Liu，Yiwen,et al. Ensemble Online Sequential Extreme Learning Machine for Air Quality Prediction[C],2021:233-237.