尹涛 YIN Tao 李秋敏 LI Qiu-min
（成都信息工程大学，成都 610100）
（Chengdu University of Information Engineering，Chengdu 610100，China）
摘要：本文利用方差选择法和互信息法抽取了财务特征中与债券违约最相关的特征，然后使用 SMOTE 和 Tomek Links 结合的方法做样本平衡处理，最后构建基于随机森林算法的债券违约预测模型，并与逻辑回归、决策树构建的模型进行了预测性能上的对比。结果显示，基于随机森林构建的模型相比于逻辑回归、决策树构建的模型，AUC、准确率这两个指标的值都更高，表明该研究在债券违约预测方面具有一定的参考价值。
Abstract: In this paper, the variance selection method and mutual information method are used to extract the financial features that are
most relevant to bond default, and then the combination of SMOTE and Tomek Links is used to balance the samples. The models
constructed by logistic regression and decision tree were compared in terms of predictive performance. The results show that the model
based on random forest has higher values of AUC and accuracy than the model built by logistic regression and decision tree, indicating that
the research has certain reference value in bond default prediction.
关键词：方差选择法；互信息法；违约预测；随机森林
Key words: variance selection method；mutual information law；default forecast；random forest
中图分类号院F832.5 文献标识码院A 文章编号院1006-4311（2023）01-120-03
DOI:10.3969/j.issn.1006-4311.2023.01.039.
文章出处：尹涛,李秋敏. 基于随机森林的债券违约预测[J]. 价值工程,2023,42(1):120-122. |
