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

Li Chaoying,Zhao En Hui,Wu Xiang Da

doi:10.1007/s00500-021-06478-7

Abstract

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.

Highlights

Reference [6] proposed a local government debt risk evaluation method based on factor analysis, this method will debt pressure, solvency, growth potential as the first-level indicators, in order to build the corresponding evaluation index system of local government debt risk, and by using factor analysis method to calculate the weight of each evaluation index, to achieve the debt risk assessment and early warning interval
In order to solve the problems existing in the above methods, this paper puts forward a new modeling method of government debt risk comprehensive evaluation based on multidimensional data mining, and verifies its application performance in government debt risk evaluation through experiments
The evaluation method based on pressure-state-response model proposed in reference [4], the evaluation method based on central point triangle whitening weight function proposed in reference [5] and the modeling method of government debt risk comprehensive evaluation based on multidimensional data mining proposed in this paper are selected as experimental comparison methods

Summary

Experimental experiment design

In order to verify the practical application effect of the modeling method of government debt risk comprehensive evaluation based on multidimensional data mining, an experimental test is carried out. The evaluation method based on pressure-state-response model proposed in reference [4], the evaluation method based on central point triangle whitening weight function proposed in reference [5] and the modeling method of government debt risk comprehensive evaluation based on multidimensional data mining proposed in this paper are selected as experimental comparison methods. By comparing the accuracy of government debt risk data mining, relative error rate and time consuming of government debt risk comprehensive evaluation of different methods, the comprehensive performance of different methods is tested

Analysis of experimental results

Findings

Conclusion