[1]刘海宏,鱼 明,刘 静,等.基于特征选择和深度学习模型的经济效益风险预测[J].南京师范大学学报(工程技术版),2024,24(04):087-92.[doi:10.3969/j.issn.1672-1292.2024.04.009]
　Liu Haihong,Yu Ming,Liu Jing,et al.Economic Benefit Risk Prediction Based on Feature Selection and Deep Learning Model[J].Journal of Nanjing Normal University(Engineering and Technology),2024,24(04):087-92.[doi:10.3969/j.issn.1672-1292.2024.04.009]
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基于特征选择和深度学习模型的经济效益风险预测
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
24卷
期数:
2024年04期
页码:
087-92
栏目:
计算机科学与技术
出版日期:
2024-12-15

文章信息/Info

Title:
Economic Benefit Risk Prediction Based on Feature Selection and Deep Learning Model
文章编号:
1672-1292(2024)04-0087-06
作者:
刘海宏1; 2; 鱼 明3; 刘 静4; 吴睿辉1; 2
(1.广州南洋理工职业学院经济管理学院,广东 广州 510900)
(2.马来西亚吉兰丹大学管理学院,马来西亚 吉兰丹州哥打巴鲁 16250)
(3.石河子大学经济与管理学院,新疆 石河子 832000)
(4.喀什大学计算机科学与技术学院,新疆 喀什 844000)
Author(s):
Liu Haihong1; 2; Yu Ming3; Liu Jing4; Wu Ruihui1; 2
(1.School of Economics and Management,Guangzhou Nanyang Polytechnic College,Guangzhou 510900,China)
(2.School of Management,Universiti Malaysia of Kelantan,Kota Bharu,Kelantan 16250,Malaysia)
(3.School of Economics and Management,Shihezi University,Shihezi 832000,China)
(4.School of Computer Science and Technology,Kashi University,Kashi 840000,China)
关键词:
经济效益风险预测; 深度学习; 特征选择; 多元宇宙优化; 双向门控递归单元
Keywords:
financial risk prediction; deep learning; feature selection; multi-verse optimization; bidirectional gated recurrent units
分类号:
TP391
DOI:
10.3969/j.issn.1672-1292.2024.04.009
文献标志码:
A
摘要:
大数据、云计算和人工智能技术的结合显著提升了企业金融数据处理能力. 为提高对中小企业经济效益风险预测的准确性和可靠性,提出了基于多元宇宙优化(multi-verse optimization,MVO)算法和双向门控递归单元(bidirectional gated recurrent units,BiGRU)的经济效益风险预测框架. 首先,对复杂金融数据进行特征归一化. 其次,使用MVO算法选出最优特征子集. 其后,利用BiGRU深度学习模型完成对中小企业经济效益风险的评估. 利用基于模型的序贯优化(sequential model-based algorithm configuration,SMAC)算法对BiGRU模型进行参数调优,优化BiGRU模型的参数配置,提高模型的性能和泛化能力. SMAC算法可以自动搜索参数空间中的最佳组合,从而找到最优的模型配置. 实验结果表明,所提混合模型在预测中小企业经济效益风险任务中表现出较高的准确性和预测能力,优于同类先进方法,证实了特征选择和深度学习模型在经济效益风险分析中的潜力和重要性.
Abstract:
The combination of big data,cloud computing and artificial intelligence technologies has significantly enhanced the capability of enterprise financial data processing. In order to improve the accuracy and reliability of financial risk prediction for small and medium-sized enterprises(SMEs),an financial risk prediction framework based on multi-verse optimization(MVO)algorithm and bidirectional gated recurrent units(BiGRU). Initially,complex financial data are subjected to feature normalization,followed by the selection of the optimal feature subset using the MVO algorithm. Subsequently,the evaluation of economic benefit risk for SMEs is accomplished using the BiGRU deep learning model. The sequential model-based algorithm configuration(SMAC)is employed to perform parameter tuning for the BiGRU model,optimizing its parameter configuration to enhance model performance and generalization ability. The SMAC algorithm automatically searches for the best combination of parameters in the parameter space,thereby identifying the optimal model configuration. Experimental results demonstrate that the proposed hybrid model exhibits higher accuracy and predictive capability in the task of predicting financial risk for SMEs,outperforming similar state-of-the-art methods,thereby confirming the potential and importance of feature selection and deep learning models in economic benefit risk analysis.

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备注/Memo

备注/Memo:
收稿日期:2024-05-14.
基金项目:广东省教育厅项目(2021GXJK595、2021TSZK021)、广东省哲学社会科学规划项目(GD22XYJ28).
通讯作者:吴睿辉,副教授,博士,研究方向:数字经济、人工智能. E-mail:liu8928114@sina.com
更新日期/Last Update: 2024-12-15