基于粒子群算法优化SVM的中小微企业信用风险评估

Credit Risk Assessment of Small,Medium and Micro Enterprises Based on Particle Swarm Optimization SVM

中小微企业融资难的问题凸显,信用风险的识别模型研究变得越来越重要,为了更好的减轻中小微企业的困境,本文以中小微企业为研究对象,选取信用风险指标,采用主成分分析(PCA)的方法筛选并构建信用风险识别指标体系。运用粒子群优化算法(PSO)与支持向量机模型相结合,优化支持向量机参数,并将该模型与网格寻优、遗传算法(GA)优化支持向量机模型的评估结果进行比较分析。最终实验结果表明:粒子群优化算法优化的支持向量机模型的评估结果准确率要优于网格寻优与遗传算法。

The problem of financing difficulties for small,medium and micro enterprises is highlighted.Research on credit risk identification models has become more and more important.In order to better alleviate the plight of small,medium and micro enterprises,this article takes small,medium and micro enterprises as the research object and selects credit risk indicators and adopts Principal component analysis(PCA)method screens and builds a credit risk identification index system.The particle swarm optimization algorithm(PSO)is combined with the support vector machine model to optimize the parameters of the support vector machine,and the model is compared with the evaluation results of the grid optimization and the genetic algorithm(GA)optimization support vector machine model.The final experimental results show that the accuracy of the evaluation results of the support vector machine model optimized by the particle swarm optimization algorithm is better than that of the grid optimization and genetic algorithm.