基于迭代自适应随机决策树的个人信用评估模型研究  

Research and Empirical Analysis on Personal Credit Evaluation Model Based on Iterative Adaptive Random Decision Tree Algorithm

作　　者：兰军[1] 严广乐[1] Jun Lan;Guangle Yan(Management School University of Shanghai for Science and Technology,Shanghai 200093 Chin)

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《经济数学》2018年第3期83-90,共8页Journal of Quantitative Economics

基　　金：上海市一流学科建设项目(S1201YLXK)

摘　　要：信用评估是商业银行控制和防范信贷风险的关键途径.决策树模型较好的直观解释性使其成为当前个人信用评估中的常用模型,但决策树模型存在容易导致过拟合且预测精度有限的问题.通过在决策树模型算法中引入类随机森林随机有放回的抽样模式,运用机器自动循环迭代寻求最优树的建模思想,建立了自适应最优C5.0决策树个人信用评估模型.该模型具有快速收敛特征变量、较好的泛化能力和高预测精度的特点,在实证分析中对商业银行个人信用评估模型质量提升带来比较明显的改进效果.Credit evaluation is a crucial approach to control and reduce credit risk. The direct expression feature of deci- sion tree model has made it a commonly used model in individual credit evaluation. However, this model has the disadvantages of over fitting and limited accuracy. By utilizing random forest sampling with replacement in decision tree model, with auto-it- eration for optimal result, an auto-fitting optimal C50 decision tree for individual credit evaluation model is established. The model has features such as rapidly converging character variables, good generalization ability and high predicting accuracy, and is shown to have phenomenal improvement on quality of individual credit evaluation in real practical analysis.

关 键 词：数量经济学 个人信用评估 决策树 随机森林 迭代 

分 类 号：N945[自然科学总论—系统科学]