Financial Early Warning Model of Electric Power Enterprises Based on Attribute Reduction Algorithm

Xiufeng Le, Dong Wang, Yongbing Yan, Shuai Zhang, Hongmei Zhang

2025

Abstract

Financial early warning model plays an important role in the finance of power enterprises, but there is the problem of inaccurate forecasting. In data analysis, attribute reduction is a process of reducing the number of features in a dataset with the aim of removing those attributes that have little impact on classification or prediction results, thereby improving data processing efficiency and reducing computational costs, while avoiding "dimensional disasters". Attribute reduction methods usually include feature selection and feature extraction. In the realm of financial management within power companies, maintaining a robust system that can accurately predict financial risks and pitfalls is paramount. One innovative approach that has gained significant traction for improving the forecasting accuracy is the implementation of attribute reduction algorithms. These algorithms are designed to simplify data sets by identifying and eliminating irrelevant or redundant attributes, which can significantly enhance the effectiveness of financial early warning systems. In this article, we will delve into the advantages, applications, and potential challenges associated with attribute reduction algorithms in the context of power enterprises' financial risk forecasting.

Paper Citation

in Harvard Style

Le X., Wang D., Yan Y., Zhang S. and Zhang H. (2025). Financial Early Warning Model of Electric Power Enterprises Based on Attribute Reduction Algorithm. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 45-50. DOI: 10.5220/0013535100004664

in Bibtex Style

@conference{incoft25,
author={Xiufeng Le and Dong Wang and Yongbing Yan and Shuai Zhang and Hongmei Zhang},
title={Financial Early Warning Model of Electric Power Enterprises Based on Attribute Reduction Algorithm},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={45-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013535100004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - Financial Early Warning Model of Electric Power Enterprises Based on Attribute Reduction Algorithm
SN - 978-989-758-763-4
AU - Le X.
AU - Wang D.
AU - Yan Y.
AU - Zhang S.
AU - Zhang H.
PY - 2025
SP - 45
EP - 50
DO - 10.5220/0013535100004664
PB - SciTePress