论文摘要
In order to improve the indicator selection method for financial early warning, this paper combines the idea of K-fold cross-validation to improve the sampling method of Random Forest（RF） and proposes the K-fold random forest algorithm（KRF）. The experimental results show that the KRF algorithm has a better classification performance than the RF algorithm, and improves the accuracy of the RF algorithm on the indicator. Finally, the importance of the selected financial indicators to the financial early warning is determined. A more scientific and accurate indicator system will provide a research basis for further financial early warning research.
论文目录
文章来源
类型: 国际会议
作者: Shi-yong XIONG,Chen LU,Liang CHANG,Ai-rong XIE
来源: 2019 International Conference on Information Technology, Electrical and Electronic Engineering (ITEEE 2019) 2019-01-20
年度: 2019
分类: 基础科学,信息科技,经济与管理科学
专业: 数学,自动化技术,企业经济
单位: Chongqing University of Posts and Telecommunications,Communication NCO Academy,Army Engineering University of PLA
分类号: F275;O212;TP18
DOI: 10.26914/c.cnkihy.2019.078540
页码: 717-722
总页数: 6
文件大小: 719k