基于特征优化和Boosting算法的个人信用预测  

Personal Credit Prediction Based on Feature Optimization and Boosting Algorithm

作　　者：常三强 周垂日[1] CHANG San-Qiang;ZHOU Chui-Ri(School of Management,University of Science and Technology of China,Hefei 230026,China)

机构地区：[1]中国科学技术大学管理学院,合肥230026

出　　处：《计算机系统应用》2023年第3期224-231,共8页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(72071188)。

摘　　要：随着互联网金融和电子支付业务的高速增长,由此引发的个人信用问题也呈现与日俱增的态势.个人信用预测本质上是不平衡的序列二分类问题,这类问题的数据样本规模大、维度高、数据分布极不平衡.为了高效区分申请者的信用情况,本文提出一种基于特征优化和集成学习的个人信用预测方法 (PL-SmoteBoost).该方法在Boosting集成框架下构建个人信用预测模型,首先利用Pearson相关系数对数据进行初始化分析,剔除冗余数据;通过Lasso选取部分特征来减少数据维度,降低高维风险;通过SMOTE过采样方法对降维数据的少数类进行线性插值,以解决类不平衡问题;最后为了验证算法有效性,以常用的处理二分类问题的算法作为对比方法,采用从Kaggle和微软开放数据库下载的高纬度不平衡数据集对算法进行测试,以AUC作为算法的评价指标,利用统计检验手段对实验结果进行分析.结果表明,相对于其他算法,本文提出的PL-SmoteBoost算法具有显著优势.With the rapid growth of Internet finance and electronic payment business, resulting personal credit problems are also increasing. Personal credit prediction is essentially an imbalanced binary sequence classification issue. Such an issue is faced with a large size and high dimension of data samples and extremely imbalanced data distribution. To effectively distinguish the credit situation of applicants, this study proposes a personal credit prediction method based on feature optimization and ensemble learning(PL-SmoteBoost). This method involves the construction of a personal credit prediction model within the boosting ensemble framework. Specifically, data initialization analysis with the Pearson correlation coefficient is conducted to eliminate redundant data;some features are selected with the least absolute shrinkage and selection operator(Lasso) to reduce data dimension and thereby lower high dimensional risks;linear interpolation among the minority classes in the dimension-reduced data is carried out by SMOTE oversampling to solve the class imbalance problem;finally, to verify the effectiveness of the proposed algorithm, this study takes the algorithms commonly used to deal with binary classification issues as comparison methods and tests the algorithms with the high dimensional imbalance datasets downloaded from the open databases of Kaggle and Microsoft. With the area under the curve(AUC) as the algorithm evaluation index, the test results are analyzed by the statistical test method. The results show that the proposed PL-SmoteBoost algorithm has significant advantages over other algorithms.

关 键 词：个人信用 SMOTE 集成学习 特征优化 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] F830.5[自动化与计算机技术—控制科学与工程]