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企业运营风险的早期预警策略:通过使用FCM集群的改进随机森林算法进行的一项研究
1School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan, Hubei, China.
PloS one
|March 11, 2025
概括
这项研究引入了一个改进的风险预警系统,使用模糊C-Means (FCM) 集群和随机森林 (RF) 模型. 这种新的方法提高了企业风险评估的准确性和速度,以提高金融稳定性.
科学领域:
- 金融风险管理 金融风险管理
- 数据科学和机器学习
- 企业风险评估 企业风险评估
背景情况:
- 传统的风险预警系统在准确性和响应速度方面面临挑战.
- 有效的企业风险评估需要强大的模型来分析各种风险指标.
研究的目的:
- 开发一种新,准确,快速的风险预警系统.
- 通过整合先进的机器学习技术,改进企业风险评估.
主要方法:
- 利用模糊C-Means (FCM) 聚类用于数据预处理和分类.
- 在风险指标加权方面采用了通过标准间相关性 (CRITIC) 的标准重要性方法.
- 开发了一个优化的随机森林 (RF) 模型,以提高预测能力.
主要成果:
- 拟议的模型实现了F1得分为87.26%,准确率为87.95%,AUC为91.20%.
- 与传统的RF模型相比,性能指标显示出显著的改善 (4.45%-6.45%).
- 在处理复杂的财务数据以预测风险方面表现出卓越的准确性和效率.
结论:
- 集成FCM集群和优化的射频模型显著提高风险预警系统的性能.
- 开发的系统为企业风险评估提供了更准确,更稳定的方法.
- 这项研究为通过先进的数据分析改进金融风险管理提供了有价值的框架.
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