|Table of Contents|

Credit risk prediction of supply chain financing enterprisesbased on IG-SVM model(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2020年01期
Page:
117-126
Research Field:
Publishing date:

Info

Title:
Credit risk prediction of supply chain financing enterprisesbased on IG-SVM model
Author(s):
Pan YongmingWang YajieLai Mingzhao
School of Management,Tianjin University of Technology,Tianjin 300384,China
Keywords:
supply chain financing information gain support vector machine credit risks classified prediction
PACS:
F832.4; F276.3
DOI:
10.14177/j.cnki.32-1397n.2020.44.01.018
Abstract:
In order to improve the accuracy of the credit risk prediction of small and medium-sized enterprises in supply chain financing,based on the research on the credit risk evaluation of small and medium-sized enterprises,a combination model which can improve the credit risk prediction is constructed by integrating machine learning algorithm. In this model,support vector machine(SVM)is used to establish the credit risk classification prediction model of small and medium-sized enterprises in the supply chain,and information gain(IG)is introduced to extract the feature variables that have significant contribution to the prediction results and optimize the feature input of the model. Compared with other models,IG-SVM model has the highest accuracy of 97.62%,which is 8.97% higher than single SVM model. Using IG for feature optimization can further improve the prediction ability of SVM model.

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Last Update: 2020-02-29