关键词: Lasso回归, 最优组合评价, Logistic回归, 贝叶斯分类, 支持向量机（SVM）

Abstract: Various types of information provided by corporate financial data can effectively explain the credit level of the company. However, too many indicators often have multicollinearity problems, resulting in the model overfitting and reducing the evaluation accuracy. In order to reduce the indicator data, the correlation coefficient between corporate default and financial indicators is calculated to eliminate indicators with weak correlation. The Lasso regression method is used to reduce the index data with high correlation. Then, three classification models of Logistic regression model, Bayesian model and support vector machine are used to classify and evaluate the credit risk of enterprises. Considering the different classification accuracy of different methods for different enterprises, in order to comprehensively utilize the advantages of each method, an optimal combination evaluation model of enterprise credit risk based on integer programming is constructed. Finally, the simulation analysis is carried out on the data of 300 companies listed on the Growth Enterprise Market. In order to verify the validity of the model, this paper randomly selects 3 groups of samples from the 300 companies（270 companies are training samples and 30 companies are test samples）. The data of one year before the special treatment（ST） is performed are tested, and the experimental results show that the combined model has higher stability and classification accuracy.

Key words: Lasso regression, optimal combination evaluation, Logistic regression, Bayesian classification, support vector machine（SVM）