@Article{CiCP-28-4,
author = {Yu, Chen and Li, Haochen and Xia, Jiangjiang and Wen, Hanqiuzi and Zhang, Pingwen},
title = {A Data-Driven Random Subfeature Ensemble Learning Algorithm for Weather Forecasting},
journal = {Communications in Computational Physics},
year = {2020},
volume = {28},
number = {4},
pages = {1305--1320},
abstract = {<p style="text-align: justify;">In this paper, the RSEL (Random Subfeature Ensemble Learning) algorithm
is proposed to improve the forecast results of weather forecasting. Based on the classical machine learning algorithms, RSEL algorithm integrates random subfeature selection and ensemble learning combination strategy to enhance the diversity of the
features and avoid the influence of a small number of unstable outliers generated randomly. Furthermore, the feature engineering schemes are designed for the weather
forecast data to make full use of spatial or temporal context. RSEL algorithm is tested
by forecasting the wind speed and direction, and it improves the forecast accuracy of
traditional methods and has good robustness.</p>},
issn = {1991-7120},
doi = {https://doi.org/ 10.4208/cicp.OA-2020-0006},
url = {https://global-sci.com/article/79720/a-data-driven-random-subfeature-ensemble-learning-algorithm-for-weather-forecasting}
}