Open AccessTheory and Application of Logistic-SVM Two-Stage Hybrid Model
Author(s) -
Pingshan Liu,
Ziming Zeng
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1746/1/012017
Subject(s) - support vector machine , logistic regression , construct (python library) , stage (stratigraphy) , computer science , machine learning , artificial intelligence , supply chain , data mining , business , marketing , paleontology , biology , programming language
Based on the theoretical analysis, this paper combined the Logistic model and the support vector machine (SVM) to construct a Logistic-SVM two-stage hybrid model for credit risk evaluation. Using the two-stage hybrid model to evaluate the credit risk of pharmaceutical supply chain finance, the results showed that the Logistic-SVM two-stage hybrid model had a higher evaluation level than the single Logistic model and the single SVM model, which verified the superiority and effectiveness of the Logistic-SVM two-stage hybrid model applied to the credit risk evaluation.