一个基于思维创新策略的北方鱼优化器,增强了破产预测问题的极端学习机器
Keyu Jiang1, Xuhai Zhao2,3, Yulin Li4,5
1Dundee International Institute of Central South University, Changsha, China.
Scientific reports
|March 12, 2026
概括
这项研究引入了一个增强的北方鱼优化器 (TIS_NGO),以改善破产风险预测. 这种新的方法优化了Kernel极端学习机 (KELM) 模型,以实现更准确的财务预测.
科学领域:
- 金融风险管理 金融风险管理
- 计算智能是一种计算智能.
- 机器学习 机器学习
背景情况:
- 对于金融机构来说,破产风险预测至关重要.
- 传统模型面临着高维度财务数据的挑战.
- 超启发式算法为增强模型性能提供了潜力.
研究的目的:
- 通过改进的元启发算法,提出一种新的破产预测模型.
- 为了提高Kernel极端学习机器 (KELM) 的性能,用于金融风险评估.
- 为参数优化引入思想启发的战略北方鱼优化器 (TIS_NGO).
主要方法:
- 将KELM与TIS_NGO算法的集成.
- TIS_NGO的改进包括基于分歧的创新,以差异进化为灵感的猎物攻击和基于对立的边界控制.
- 在CEC2017,CEC2022基准套件和维斯拉夫破产数据集上评估模型性能.
主要成果:
- 与标准的非政府组织,公共服务机构和GWO相比,TIS_NGO表现出更高的融合速度和解决方案准确性.
- 针对TIS_NGO优化的KELM在破产数据集上实现了高分类准确性和稳定性.
- 验证了将先进的元启发学与机器学习用于财务预测的有效性.
结论:
- 提议的TIS_NGO-KELM模型在破产预测准确性和稳定性方面提供了有前途的进展.
- 这种方法为金融领域的早期预警系统提供了新的技术途径.
- 强调了改进的元启发算法在金融风险管理中的潜力.
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