Research on modeling of government debt risk comprehensive evaluation based on multidimensional data mining

In order to solve the problems of low accuracy of data mining, high relative error rate of evaluation, and long time of evaluation in traditional government debt risk evaluation methods, this paper proposes a modeling method of government debt risk comprehensive evaluation based on multidimensional...

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Bibliographic Details
Published inSoft computing (Berlin, Germany) Vol. 26; no. 16; pp. 7493 - 7500
Main Authors ChaoYing, Li, Da, Wu Xiang, Hui, Zhao En
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2022
Subjects
Artificial Intelligence
Computational Intelligence
Control
Engineering
Focus
Mathematical Logic and Foundations
Mechatronics
Robotics
KMV model
Government debt risk
Multidimensional data mining
Comprehensive evaluation
Evaluation modeling
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Summary:In order to solve the problems of low accuracy of data mining, high relative error rate of evaluation, and long time of evaluation in traditional government debt risk evaluation methods, this paper proposes a modeling method of government debt risk comprehensive evaluation based on multidimensional data mining. The MAFIA algorithm is used for multidimensional mining of government debt risk data, and K-means clustering algorithm is used for clustering processing of mined data. The KMV model is built based on the clustering findings, and the uncertainty factor is utilized to alter the model in order to provide a complete assessment of government debt risk using the modified KMV model. The experimental results show that the accuracy rate of government debt risk data mining is always above 91%, the relative error rate of evaluation is always below 3.4%, and the average evaluation time is 0.71 s, the practical application effect is good.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-021-06478-7