吉林大学学报（理学版）2018，Vol.56Issue(1)：119-125,7.DOI:10.13413/j.cnki.jdxblxb.2018.01.20
基于粒子群协同优化算法的供应链金融信用风险评价模型
Financial Credit Risk Evaluation Model of Supply Chain Finance Based on Particle Swarm Cooperative Optimization Algorithm
摘要
Abstract
Aiming at the problem that the accuracy of credit risk evaluation of supply chain finance mode was affected by credit feature subset and model parameters ,we proposed a credit risk evaluation model with particle swarm cooperative optimization . On the basis of fully demonstrating the characteristic index system of supply chain financial risk ,we used the binary particle swarm algorithm to optimize the feature subset and optimize parameters of support vector machines .We carried out an experiment on the risk evaluation of supply chain financial credit ,and compared it with traditional radial basis support vector machines and feature extraction method of principal component analysis . The results show that the selected feature subset and SVM parameters of the proposed model cansignificantly improve the accuracy of credit risk evaluation .关键词
供应链金融/信用风险评价/粒子群算法/支持向量机Key words
supply chain finance/credit risk evaluation/particle swarm algorithm/support vector m achine (S V M )分类
信息技术与安全科学引用本文复制引用
刘颖,张丽娟,韩亚男,庞丽艳,王帅..基于粒子群协同优化算法的供应链金融信用风险评价模型[J].吉林大学学报（理学版）,2018,56(1):119-125,7.基金项目
国家自然科学基金(批准号:61402193 （）
61572225)、长春市地院(校、所)合作专项基金(批准号:17DY009)、国家社会科学基金(批准号:16BGL180)、物流产业经济与智能物流吉林省高校重点实验室开放基金(批准号:201702)和吉林省教育厅"十二五"社会科学研究规划项目(批准号:2015363). （校、所）
