Research on Optimization of Random Forest Algorithm Based on Feature Engineering

Lei Yang, Yong Fan, Yungui Chen

2023

Abstract

Random forest is an ensemble learning method that builds a strong classifier by combining multiple weak classifiers. Feature engineering refers to the process of improving model performance by selecting and manipulating features. This paper selects 3 A-share stocks of China's Shanghai Stock Exchange as the research object, and proposes a prediction model based on the random forest optimization algorithm of feature engineering, and uses this model to predict the closing price of the stock. Our research results show that the optimized model performance of predictions can be improved.

Paper Citation

in Harvard Style

Yang L., Fan Y. and Chen Y. (2023). Research on Optimization of Random Forest Algorithm Based on Feature Engineering. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 34-38. DOI: 10.5220/0012273400003807

in Bibtex Style

@conference{anit23,
author={Lei Yang and Yong Fan and Yungui Chen},
title={Research on Optimization of Random Forest Algorithm Based on Feature Engineering},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={34-38},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012273400003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Research on Optimization of Random Forest Algorithm Based on Feature Engineering
SN - 978-989-758-677-4
AU - Yang L.
AU - Fan Y.
AU - Chen Y.
PY - 2023
SP - 34
EP - 38
DO - 10.5220/0012273400003807
PB - SciTePress