一种新的基于政治优化器的金融危机预测特征选择和最优机器学习模型

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Swathy Vodithala, Raghuram Bhukya
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引用次数: 0

摘要

在当今的数字环境中,商业智能的进步使其难以保持竞争力和跟上商业趋势。金融业的决策越来越受到大数据和机器学习的推动。决策过程可以被认为是个人为了选择最适合满足其需求和必要性的选项或行动方案而经历的任何一系列过程。预测金融危机爆发的能力是一种重要的经济现象。一个国家的经济发展和实力可以通过其准确评估失败公司数量和失败频率的能力来衡量。全球经济遭受了最近的全球危机的破坏,如新冠肺炎大流行和其他最近的环境、金融和经济灾难,这些危机使建设可维持的经济和文明的努力边缘化。一个国家经济的健康和增长可以通过准确估计将失败的企业数量和将成功的企业数量来决定。从历史上看,有许多策略可以构建成功的金融危机预测(FPC)方法。有效预测企业倒闭是衡量一个国家经济健康状况的指标。有几种策略可用于有效的FCP。分类性能、预测准确性和合法性不足以用于实际应用。建议的几种方法适用于某些问题。特定数据集不可扩展。为了改进分类,设计一个适用于多个数据集的良好预测模型。一个有效的金融危机预测方法(FPC)需要正确的品质。ML模型还可以用于对公司的财务健康状况进行分类。本研究针对大数据环境下的FCP,提出了基于政治优化器的特征选择(POFS)和最优级联深度森林(OCDF)。Hadoop Map Reduce处理庞大的数据集。POFS通过处理特征选择来降低计算复杂性。POFS是一种使用OCDF的FCP分类算法。SFO用于优化CDF模型参数。进行了一项全面的模拟研究,以评估POFS在基准数据集OCDF上的性能。结果证实了POFS-OCDF方法优于最先进的方法。所提出的POFS-OCDF技术具有0.912的突出灵敏度、0.953的特异性、0.944的准确度、0.930的F评分和0.912的Matthews相关系数,显示出最佳结果。实验结果表明,POFS-OCDF技术在各种标准上都优于其他最近开发的策略。如前所述,向日葵优化(SFO)也用于调整级联深林(CDF)参数。基于基准数据集进行了详细的仿真分析,以评估POFS-OCDF技术的更高分类效率。FCP的POFS算法的发明体现了这项工作的独创性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Political Optimizer-Based Feature Selection with an Optimal Machine Learning Model for Financial Crisis Prediction
In today’s digital environment, business intelligence advances make it difficult to stay competitive and up to date on business trends. Decision-making in the financial industry is increasingly being powered by big data and machine learning. A decision-making process may be thought of as any sequence of processes that an individual goes through in order to select the option or course of action that is most suitable to meet their needs and necessities. The ability to anticipate the onset of a financial crisis is a significant economic phenomenon. A nation’s economic development and strength can be gauged by its capacity to provide an accurate assessment of the number of failed firms and the frequency with which they fail. The economics of the globe have been ravaged by recent global crises like as the COVID-19 pandemic and other recent environmental, financial, and economic disasters, which have marginalized efforts to construct a maintainable economy and civilization. The health and growth of a nation’s economy can be determined by precisely estimating the number of enterprises that will fail and the number that will succeed. Historically, there have been numerous strategies for constructing a successful financial crisis prediction (FPC) method. Effectively predicting business failures is a gauge of a country’s economic health. Several strategies are available for effective FCP. Classification performance, forecast accuracy, and legality are insufficient for practical use. Several of the suggested methods work for some issues. The specific dataset is not expandable. To improve classification, design a good prediction model adaptable to several datasets. An effective financial crisis prediction method (FPC) requires the right qualities. ML models can also be used to classify a company’s financial health. This research presents political optimizer-based feature selection (POFS) with optimal cascaded deep forest (OCDF) for FCP in big data environments. Hadoop Map Reduce handles huge datasets. POFS reduces computing complexity by handling feature selection. POFS is an original FCP algorithm categorization using OCDF. SFO is used to optimize CDF model parameters. A thorough simulation study was performed to evaluate POFS performance on benchmark datasets OCDFs. The results confirmed the POFS-OCDF method’s superiority over state-of-the-art approaches. With an outstanding sensitivity of 0.912, specificity of 0.953, accuracy of 0.944, F-score of 0.930, and Matthews correlation coefficient (MCC) of 0.912, the proposed POFS-OCDF technique has shown optimum results. The experimental results demonstrated that the POFS-OCDF technique outperformed other recently developed strategies on a variety of criteria. As previously stated, Sunflower optimization (SFO) is also used to tune the Cascaded Deep Forest (CDF) parameters. A detailed simulation analysis is performed based on the benchmark dataset to evaluate the higher classification efficiency of the POFS-OCDF technique. The invention of the POFS algorithm for FCP exemplifies the work’s originality.
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来源期刊
International Journal of Cooperative Information Systems
International Journal of Cooperative Information Systems 工程技术-计算机:信息系统
CiteScore
2.30
自引率
0.00%
发文量
8
审稿时长
>12 weeks
期刊介绍: The paradigm for the next generation of information systems (ISs) will involve large numbers of ISs distributed over large, complex computer/communication networks. Such ISs will manage or have access to large amounts of information and computing services and will interoperate as required. These support individual or collaborative human work. Communication among component systems will be done using protocols that range from conventional ones to those based on distributed AI. We call such next generation ISs Cooperative Information Systems (CIS). The International Journal of Cooperative Information Systems (IJCIS) addresses the intricacies of cooperative work in the framework of distributed interoperable information systems. It provides a forum for the presentation and dissemination of research covering all aspects of CIS design, requirements, functionality, implementation, deployment, and evolution.
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