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An improved gradient boosting tree algorithm for financial risk management
Knowledge Management Research & Practice ( IF 3.054 ) Pub Date : 2021-07-16 , DOI: 10.1080/14778238.2021.1954489
Srijana Acharya 1 , Irina V. Pustokhina 2 , Denis A. Pustokhin 3 , B. T. Geetha 4 , Gyanendra Prasad Joshi 5 , Jamel Nebhen 6 , Eunmok Yang 7 , Changho Seo 1
Affiliation  

ABSTRACT

Financial risk assessment (FRA) is an essential process in financial institutions determining a company’s creditworthiness. This paper introduces a new wrapper feature selection with a clustering-based FRA model to assess the financial status. This study involves three different phases of operations such as feature selection, clustering, and classification. The proposed model initially designs an Information Gain Directed Feature Selection algorithm that offers to rank to the features utilising the information gain. In addition, the proposed model also involves an improved K-means clustering technique to cluster the data. Finally, the gradient boosting tree classifier model is executed to perform the classification process. The proposed model tested using two benchmark datasets. The simulation results indicate that the projected FRA model obtains maximum accuracy values of 95.68% and 94.76% on the applied datasets.



中文翻译:

一种用于金融风险管理的改进梯度提升树算法

摘要

金融风险评估 (FRA) 是金融机构确定公司信誉的重要过程。本文介绍了一种新的包装器特征选择,使用基于聚类的 FRA 模型来评估财务状况。这项研究涉及三个不同的操作阶段,例如特征选择、聚类和分类。所提出的模型最初设计了一种信息增益定向特征选择算法,该算法利用信息增益对特征进行排序。此外,所提出的模型还涉及一种改进的 K-means 聚类技术来对数据进行聚类。最后,执行梯度提升树分类器模型来执行分类过程。所提出的模型使用两个基准数据集进行测试。

更新日期:2021-07-16
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